configs

configs

Configuration module for MolmoSpaces experiments.

Import config classes from their own submodules, not from this package:

• abstract_config: Base Config class
• abstract_exp_config: Base experiment configuration
• camera_configs: Camera-related configurations
• robot_configs: Robot-related configurations
• task_configs: Task-related configurations
• task_sampler_configs: Task sampler-related configurations
• policy_configs: Policy-related configurations

This file is deliberately kept free of imports. Python runs a package's init before any of its submodules, so re-exporting the config tree here meant that importing any molmo_spaces.configs.<x> -- from anywhere -- eagerly pulled in the exp/policy config tree, the policy layer and the task layer (83 modules, ~2s). Besides the cost, it made a genuine import cycle reachable: configs/init -> policy_configs -> policy.base_policy -> tasks.task -> env.env, which fails whenever env/env.py is the module entered first (as it is for a cold import molmo_spaces.tasks.pick_task_sampler). Keeping this empty means a submodule import costs only that submodule.

Modules:

Name	Description
abstract_config
abstract_exp_config
base_motion_config	Base-motion evaluation config: how well a robot moves from one object to another.
base_nav_to_obj_config	Example configuration for RBY1 navigation to object data generation using the extracted task sampler.
base_open_task_configs
base_packing_configs
base_pick_and_place_color_configs
base_pick_and_place_configs
base_pick_and_place_next_to_configs
base_pick_config	Example configuration for Franka pick-and-place data generation using the extracted task sampler.
camera_configs
dummy_config
policy_configs	Policy configuration classes for MolmoSpaces experiments.
policy_configs_baselines
policy_configs_fetchman_pick	Configuration for :class:G1PickPlannerPolicy (the "Fetchman" G1 pick).
robot_configs	Robot configuration classes for MolmoSpaces experiments.
task_configs	Task configuration classes for MolmoSpaces experiments.
task_sampler_configs	Task sampler configuration classes for MolmoSpaces experiments.

abstract_config

Classes:

Name	Description
Config	Base configuration class that can be extended for specific configurations.

Config

Bases: BaseModel

Base configuration class that can be extended for specific configurations. Provides methods to convert to dict, json, and to save/load from files.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
model_config

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

abstract_exp_config

Classes:

Name	Description
MlSpacesExpConfig	Base configuration class for experiments.

Attributes:

Name	Type	Description
log

log module-attribute

log = logging.getLogger(__name__)

MlSpacesExpConfig

Bases: Config, ABC

Base configuration class for experiments.

This should be extended to create specific experiment configurations.

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms instance-attribute

ctrl_dt_ms: float

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs instance-attribute

num_envs: int

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config instance-attribute

policy_config: BasePolicyConfig

Configuration for policies

policy_dt_ms instance-attribute

policy_dt_ms: float

Default policy time step

profile class-attribute instance-attribute

profile: bool = False

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset instance-attribute

scene_dataset: str

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms instance-attribute

sim_dt_ms: float

Default simulation time step

tag abstractmethod property

tag: str

A string describing the experiment.

task_config instance-attribute

task_config: AllTaskConfigs

Configuration for tasks

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = None

Maximum number of steps per episode (if None, no time limit)

task_sampler_config instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig

Configuration for the task sampler

task_type instance-attribute

task_type: str

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer instance-attribute

use_passive_viewer: bool

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict instance-attribute

viewer_cam_dict: dict

Dictionary containing viewer camera parameters

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = None

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def model_post_init(self, _context: Any) -> None:
    def is_multiple(val_a: float, val_b: float) -> bool:
        if val_b == 0:
            return val_a == 0

        multiplier = round(val_a / val_b)
        return math.isclose(val_a, multiplier * val_b)

    assert is_multiple(self.policy_dt_ms, self.ctrl_dt_ms), (
        "policy_dt_ms must be a multiple of ctrl_dt_ms"
    )
    assert is_multiple(self.ctrl_dt_ms, self.sim_dt_ms), (
        "ctrl_dt_ms must be a multiple of sim_dt"
    )

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_motion_config

Base-motion evaluation config: how well a robot moves from one object to another.

Reuses NavToObjTask/NavToObjTaskSampler entirely -- the only difference from plain nav_to_obj is start_near_object_probability=100, which makes the sampler place the robot near a randomly chosen scene object (2-5m from the nav target by default) instead of near the nav target itself. See NavToObjTaskSampler._sample_start_object_near.

Classes:

Name	Description
BaseMotionBaseConfig	Base configuration for base-motion (object-to-object locomotion) evaluation.

BaseMotionBaseConfig

Bases: NavToObjBaseConfig

Base configuration for base-motion (object-to-object locomotion) evaluation.

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Camera configuration - using new unified camera system
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_threads	int
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
record_videos	bool
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset	NavToObjTaskConfig | None
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	NavToObjTaskSamplerConfig	Task sampler configuration (imported from task_sampler_configs.py)
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = RBY1MjcfCameraSystem()

Camera configuration - using new unified camera system

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_threads class-attribute instance-attribute

num_threads: int = 1

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = AStarNavToObjPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 200.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

record_videos class-attribute instance-attribute

record_videos: bool = False

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = NavToObjTaskConfig(task_cls=NavToObjTask)

Configuration for tasks

task_config_preset class-attribute instance-attribute

task_config_preset: NavToObjTaskConfig | None = None

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: NavToObjTaskSamplerConfig = NavToObjTaskSamplerConfig(task_sampler_class=NavToObjTaskSampler, start_near_object_probability=0, sample_trajectory_obstacles=True, num_trajectory_obstacles=10)

Task sampler configuration (imported from task_sampler_configs.py)

task_type class-attribute instance-attribute

task_type: str = 'base_motion'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': np.array([0.0, 0.0, 0.5])}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = None

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_nav_to_obj_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_nav_to_obj_config

Example configuration for RBY1 navigation to object data generation using the extracted task sampler. This shows how the scene randomization functionality from the reference script has been properly integrated into the modular task sampler architecture.

Classes:

Name	Description
NavToObjBaseConfig	Base configuration for navigation to object data generation tasks.

NavToObjBaseConfig

Bases: MlSpacesExpConfig

Base configuration for navigation to object data generation tasks.

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Camera configuration - using new unified camera system
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_threads	int
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
record_videos	bool
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset	NavToObjTaskConfig | None
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Task sampler configuration (imported from task_sampler_configs.py)
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = RBY1MjcfCameraSystem()

Camera configuration - using new unified camera system

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_threads class-attribute instance-attribute

num_threads: int = 1

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = AStarNavToObjPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 200.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

record_videos class-attribute instance-attribute

record_videos: bool = False

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = NavToObjTaskConfig(task_cls=NavToObjTask)

Configuration for tasks

task_config_preset class-attribute instance-attribute

task_config_preset: NavToObjTaskConfig | None = None

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = NavToObjTaskSamplerConfig(task_sampler_class=NavToObjTaskSampler)

Task sampler configuration (imported from task_sampler_configs.py)

task_type class-attribute instance-attribute

task_type: str = 'nav_to_obj'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': np.array([0.0, 0.0, 0.5])}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = None

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_nav_to_obj_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_open_task_configs

Classes:

Name	Description
ClosingBaseConfig	Base configuration for closing task data generation.
OpeningBaseConfig	Base configuration for opening task data generation.

ClosingBaseConfig

Bases: PickBaseConfig

Base configuration for closing task data generation.

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset	OpeningTaskConfig | None
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = FrankaRandomizedD405D455CameraSystem()

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = OpenClosePlannerPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 66.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = OpeningTaskConfig(task_cls=OpeningTask, task_success_threshold=0.85, joint_index=0, any_inst_of_category=False)

Configuration for tasks

task_config_preset class-attribute instance-attribute

task_config_preset: OpeningTaskConfig | None = None

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = OpenTaskSamplerConfig(task_sampler_class=OpenTaskSampler, target_initial_state_open_percentage=0.5)

Configuration for the task sampler

task_type class-attribute instance-attribute

task_type: str = 'close'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': [0.0, 0.0, 0.5]}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = 'molmo-spaces-data-generation'

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_pick_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    # Auto-create profiler instance if profiling is enabled
    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

OpeningBaseConfig

Bases: PickBaseConfig

Base configuration for opening task data generation.

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset	OpeningTaskConfig | None
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = FrankaRandomizedD405D455CameraSystem()

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = OpenClosePlannerPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 66.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = OpeningTaskConfig(task_cls=OpeningTask, task_success_threshold=0.15, joint_index=0, any_inst_of_category=True)

Configuration for tasks

task_config_preset class-attribute instance-attribute

task_config_preset: OpeningTaskConfig | None = None

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = OpenTaskSamplerConfig(task_sampler_class=OpenTaskSampler, target_initial_state_open_percentage=0)

Configuration for the task sampler

task_type class-attribute instance-attribute

task_type: str = 'open'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': [0.0, 0.0, 0.5]}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = 'molmo-spaces-data-generation'

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_pick_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    # Auto-create profiler instance if profiling is enabled
    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_packing_configs

Classes:

Name	Description
PackingDataGenConfig

PackingDataGenConfig

Bases: PickBaseConfig

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = FrankaRandomizedD405D455CameraSystem()

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = PickAndPlacePlannerPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 66.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = PackingTaskConfig(task_cls=PackingTask)

Configuration for tasks

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = PackingTaskSamplerConfig(task_sampler_class=PackingTaskSampler, pickup_types=PICK_AND_PLACE_OBJECTS, samples_per_house=20)

Configuration for the task sampler

task_type class-attribute instance-attribute

task_type: str = 'packing'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': [0.0, 0.0, 0.5]}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = 'molmo-spaces-data-generation'

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_pick_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    # Auto-create profiler instance if profiling is enabled
    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_pick_and_place_color_configs

Classes:

Name	Description
PickAndPlaceColorDataGenConfig

PickAndPlaceColorDataGenConfig

Bases: PickBaseConfig

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = FrankaRandomizedD405D455CameraSystem()

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir class-attribute instance-attribute

output_dir: Path = ASSETS_DIR / 'experiment_output' / 'datagen' / 'pick_and_place_color_base_v1'

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = PickAndPlaceColorPlannerPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 66.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = PickAndPlaceColorTaskConfig(task_cls=PickAndPlaceColorTask)

Configuration for tasks

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = PickAndPlaceColorTaskSamplerConfig(task_sampler_class=PickAndPlaceColorTaskSampler, samples_per_house=20)

Configuration for the task sampler

task_type class-attribute instance-attribute

task_type: str = 'pick_and_place_color'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': [0.0, 0.0, 0.5]}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = 'molmo-spaces-data-generation'

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_pick_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    # Auto-create profiler instance if profiling is enabled
    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_pick_and_place_configs

Classes:

Name	Description
PickAndPlaceDataGenConfig

PickAndPlaceDataGenConfig

Bases: PickBaseConfig

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = FrankaRandomizedD405D455CameraSystem()

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = PickAndPlacePlannerPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 66.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = PickAndPlaceTaskConfig(task_cls=PickAndPlaceTask)

Configuration for tasks

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = PickAndPlaceTaskSamplerConfig(task_sampler_class=PickAndPlaceTaskSampler, pickup_types=[], samples_per_house=20)

Configuration for the task sampler

task_type class-attribute instance-attribute

task_type: str = 'pick_and_place'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': [0.0, 0.0, 0.5]}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = 'molmo-spaces-data-generation'

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_pick_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    # Auto-create profiler instance if profiling is enabled
    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_pick_and_place_next_to_configs

Classes:

Name	Description
PickAndPlaceNextToDataGenConfig

PickAndPlaceNextToDataGenConfig

Bases: PickBaseConfig

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = FrankaRandomizedD405D455CameraSystem()

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = PickAndPlaceNextToPlannerPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 66.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig = FrankaRobotConfig()

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = PickAndPlaceNextToTaskConfig(task_cls=PickAndPlaceNextToTask)

Configuration for tasks

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = PickAndPlaceNextToTaskSamplerConfig(task_sampler_class=PickAndPlaceNextToTaskSampler, pickup_types=PICK_AND_PLACE_OBJECTS, samples_per_house=20)

Configuration for the task sampler

task_type class-attribute instance-attribute

task_type: str = 'pick_and_place_next_to'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': [0.0, 0.0, 0.5]}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = 'molmo-spaces-data-generation'

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_pick_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    # Auto-create profiler instance if profiling is enabled
    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

base_pick_config

Example configuration for Franka pick-and-place data generation using the extracted task sampler. This shows how the scene randomization functionality from the reference script has been properly integrated into the modular task sampler architecture.

Classes:

Name	Description
PickBaseConfig

PickBaseConfig

Bases: MlSpacesExpConfig

Classes:

Name	Description
SavedEpisode	Config information describing a single episode

Methods:

Name	Description
freeze_task_config
from_dict
load_config
load_from_json
model_post_init
save_config
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
CameraConfig	type
PolicyConfig	type
RobotConfig	type
benchmark_path	Path | None	Contains a json with a list of fully-specified episodes
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
collision_free_pose_limit	int
config_version	str
ctrl_dt_ms	float	Default control time step
data_split	str	Data split to use, e.g. train, val, test
datagen_profiler	bool	Whether or not to use the datagen profiler
end_on_success	bool	Whether to end episode immediately upon success (overrides task_horizon if True)
environment_light_intensity	float	The environment intensity value for the IBL when using the filament-based renderer
eval_runtime_params	Any	Evaluation runtime parameters (optional, set during evaluation initialization)
filter_for_successful_trajectories	bool	If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory
fps	float
log_level	str	Global logging level: "debug", "info", "warning", "error", "none
model_config
no_cached_map	bool	Whether or not to cache the generated thormap to disk after creating it for a datagen run
num_envs	int	Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)
num_workers	int	Number of worker processes for parallel data generation (episode-level parallelism)
output_dir	Path	Output directory for experiment results
policy_config	BasePolicyConfig	Configuration for policies
policy_dt_ms	float	Default policy time step
profile	bool	Whether to enable profiling
profiler	Profiler | None	Profiler instance (auto-created if profile=True)
robot_config	BaseRobotConfig	Configuration for the robot
save_lerobot	bool
scene_dataset	str	Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config
seed	int | None	Random seed for task sampling (if None, generates random seed)
sim_dt_ms	float	Default simulation time step
tag	str	A string describing the experiment.
task_config	AllTaskConfigs	Configuration for tasks
task_config_preset_exp	AllTaskConfigs | None	Cached config for whole experiment
task_config_preset_scn	AllTaskConfigs | None	Cached config for scene
task_horizon	int | None	Maximum number of steps per episode (if None, no time limit)
task_sampler_config	BaseMujocoTaskSamplerConfig	Configuration for the task sampler
task_type	str	Task type: e.g. pick, pick_and_place, etc.
use_passive_viewer	bool	Launch passive viewer for rendering
use_wandb	bool	Whether or not to use wandb logging
viewer_cam_dict	dict	Dictionary containing viewer camera parameters
viewer_camera	None
wandb_name	str | None	(Optional) The name of the wandb run
wandb_project	str | None	(Optional) The name of the wandb project to use

CameraConfig class-attribute

CameraConfig: type = CameraSystemConfig

PolicyConfig class-attribute

PolicyConfig: type = BasePolicyConfig

RobotConfig class-attribute

RobotConfig: type = BaseRobotConfig

benchmark_path class-attribute instance-attribute

benchmark_path: Path | None = None

Contains a json with a list of fully-specified episodes

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = FrankaRandomizedD405D455CameraSystem()

Configuration for cameras and sensors

collision_free_pose_limit class-attribute instance-attribute

collision_free_pose_limit: int = 3

config_version class-attribute instance-attribute

config_version: str = '0.1'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 2.0

Default control time step

data_split class-attribute instance-attribute

data_split: str = 'train'

Data split to use, e.g. train, val, test

datagen_profiler class-attribute instance-attribute

datagen_profiler: bool = True

Whether or not to use the datagen profiler

end_on_success class-attribute instance-attribute

end_on_success: bool = False

Whether to end episode immediately upon success (overrides task_horizon if True)

environment_light_intensity class-attribute instance-attribute

environment_light_intensity: float = 15000.0

The environment intensity value for the IBL when using the filament-based renderer

eval_runtime_params class-attribute instance-attribute

eval_runtime_params: Any = None

Evaluation runtime parameters (optional, set during evaluation initialization)

filter_for_successful_trajectories class-attribute instance-attribute

filter_for_successful_trajectories: bool = True

If True, only save successful trajectories to main output directory (failed episodes may be sampled 1% for debug directory). If False, save all trajectories to main output directory

fps property

fps: float

log_level class-attribute instance-attribute

log_level: str = 'info'

Global logging level: "debug", "info", "warning", "error", "none

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

no_cached_map class-attribute instance-attribute

no_cached_map: bool = False

Whether or not to cache the generated thormap to disk after creating it for a datagen run

num_envs class-attribute instance-attribute

num_envs: int = 1

Number of batched environments per worker (for vectorized physics in CPUMujocoEnv)

num_workers class-attribute instance-attribute

num_workers: int = 1

Number of worker processes for parallel data generation (episode-level parallelism)

output_dir instance-attribute

output_dir: Path

Output directory for experiment results

policy_config class-attribute instance-attribute

policy_config: BasePolicyConfig = PickPlannerPolicyConfig()

Configuration for policies

policy_dt_ms class-attribute instance-attribute

policy_dt_ms: float = 66.0

Default policy time step

profile class-attribute instance-attribute

profile: bool = True

Whether to enable profiling

profiler class-attribute instance-attribute

profiler: Profiler | None = None

Profiler instance (auto-created if profile=True)

robot_config instance-attribute

robot_config: BaseRobotConfig

Configuration for the robot

save_lerobot class-attribute instance-attribute

save_lerobot: bool = False

scene_dataset class-attribute instance-attribute

scene_dataset: str = 'procthor-10k'

Scenes to use, e.g. ithor, procthor-10k, procthor-objaverse. If "user", use the scene_xml_paths in task_sampler_config

seed class-attribute instance-attribute

seed: int | None = None

Random seed for task sampling (if None, generates random seed)

sim_dt_ms class-attribute instance-attribute

sim_dt_ms: float = 2.0

Default simulation time step

tag property

tag: str

A string describing the experiment.

task_config class-attribute instance-attribute

task_config: AllTaskConfigs = PickTaskConfig(task_cls=PickTask)

Configuration for tasks

task_config_preset_exp class-attribute instance-attribute

task_config_preset_exp: AllTaskConfigs | None = None

Cached config for whole experiment

task_config_preset_scn class-attribute instance-attribute

task_config_preset_scn: AllTaskConfigs | None = None

Cached config for scene

task_horizon class-attribute instance-attribute

task_horizon: int | None = 500

Maximum number of steps per episode (if None, no time limit)

task_sampler_config class-attribute instance-attribute

task_sampler_config: BaseMujocoTaskSamplerConfig = PickTaskSamplerConfig(task_sampler_class=PickTaskSampler)

Configuration for the task sampler

task_type class-attribute instance-attribute

task_type: str = 'pick'

Task type: e.g. pick, pick_and_place, etc.

use_passive_viewer class-attribute instance-attribute

use_passive_viewer: bool = False

Launch passive viewer for rendering

use_wandb class-attribute instance-attribute

use_wandb: bool = False

Whether or not to use wandb logging

viewer_cam_dict class-attribute instance-attribute

viewer_cam_dict: dict = {'distance': 5.0, 'azimuth': 45.0, 'elevation': -30.0, 'lookat': [0.0, 0.0, 0.5]}

Dictionary containing viewer camera parameters

viewer_camera class-attribute instance-attribute

viewer_camera: None = None

wandb_name class-attribute instance-attribute

wandb_name: str | None = None

(Optional) The name of the wandb run

wandb_project class-attribute instance-attribute

wandb_project: str | None = 'molmo-spaces-data-generation'

(Optional) The name of the wandb project to use

SavedEpisode

Bases: Config

Config information describing a single episode

The code below this is used for saving episode state so that it can be re-loaded w/o sampling

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_config	CameraSystemConfig | None	Configuration for cameras and sensors
model_config
robot_config	BaseRobotConfig | None	Configuration for the robot
task_cls_str	str | None	(Optional) The name of the task class to be used
task_config	AllTaskConfigs | None	Configuration for tasks

camera_config class-attribute instance-attribute

camera_config: CameraSystemConfig | None = None

Configuration for cameras and sensors

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

robot_config class-attribute instance-attribute

robot_config: BaseRobotConfig | None = None

Configuration for the robot

task_cls_str class-attribute instance-attribute

task_cls_str: str | None = None

(Optional) The name of the task class to be used

task_config class-attribute instance-attribute

task_config: AllTaskConfigs | None = None

Configuration for tasks

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

freeze_task_config

freeze_task_config(observation, task: BaseMujocoTask) -> str

Source code in molmo_spaces/configs/abstract_exp_config.py

def freeze_task_config(self, observation, task: BaseMujocoTask) -> str:
    sc = MlSpacesExpConfig.SavedEpisode()

    # NOTE(RMH): deep argument VERY IMPORTANT. Mutates config for future episodes otherwise
    sc.robot_config = self.robot_config.model_copy(deep=True)

    sc.robot_config.robot_cls = None
    sc.robot_config.robot_factory = None
    sc.robot_config.robot_view_factory = None

    sc.robot_config.init_qpos_noise_range = None
    sc.robot_config.init_qpos = observation[0]["qpos"]

    # TODO(wilbert): by now the camera_config shouldn't be None (must be set by the user). It
    # might need to be declared as not optional instead
    assert self.camera_config is not None, "Must have a valid 'self.camera_config' by now"

    sc.camera_config = self.camera_config.model_copy(deep=True)
    for i, camera in enumerate(sc.camera_config.cameras):
        # Some cameras can contain random sampling, e.g. of positions
        # Read the camera's positions and convert them to fixed cameras
        if isinstance(camera, MjcfCameraConfig | RobotMountedCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]

            # TODO(wilbert): uhmm, most of the attributes below are only defined in the
            # RobotMountedCamera cls, not the base Camera cls. For now we're assumming
            # that here we always get a RobotMountedCamera instance, otherwise it would
            # crash anyway bc the parameters expected wouldn't be defined
            assert isinstance(cam, RobotMountedCamera), (
                "Something is wrong here, the 'cam' instance should be of class 'RobotMountedCamera'"
            )

            camera_quat: list[float] = (
                cam.camera_quaternion.tolist()
                if cam.camera_quaternion is not None
                else [1.0, 0.0, 0.0, 0.0]
            )
            new_camera = RobotMountedCameraConfig(
                name=cam.name,
                reference_body_names=list(cam.reference_body_names),
                camera_offset=list(cam.camera_offset),
                lookat_offset=list(cam.lookat_offset),
                camera_quaternion=camera_quat,
                fov=cam.fov,
            )
            sc.camera_config.cameras[i] = new_camera

        elif isinstance(camera, RandomizedExocentricCameraConfig | FixedExocentricCameraConfig):
            cam = task.env.camera_manager.registry[camera.name]
            new_camera = FixedExocentricCameraConfig(
                name=cam.name,
                fov=cam.fov,
                pos=list(cam.pos),
                up=list(cam.up),
                forward=list(cam.forward),
            )
            sc.camera_config.cameras[i] = new_camera
        else:
            raise NotImplementedError(f"Cannot freeze camera of type {type(camera).__name__}")

    obj_poses = {}
    om = task.env.object_managers[task.env.current_batch_index]
    task_objects = om.get_mobile_objects()
    for task_object in task_objects:
        obj_poses[task_object.name] = pose_mat_to_7d(task_object.pose).tolist()
    task.config.task_config.object_poses = obj_poses

    sc.task_config = self.task_config.model_copy(deep=True)
    sc.task_config.task_cls = None
    if self.task_config.task_cls is not None:
        sc.task_cls_str = (
            self.task_config.task_cls.__module__ + "." + self.task_config.task_cls.__name__
        )

    if sc.task_config and sc.task_config.robot_base_pose is None:
        raise RuntimeError("'robot_base_pose' attribute must be in the task_config")

    sc_bytes = pickle.dumps(sc)
    sc_b64 = base64.b64encode(sc_bytes).decode("utf-8")
    return sc_b64

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_config staticmethod

load_config(output_dir: Path) -> MlSpacesExpConfig

Source code in molmo_spaces/configs/abstract_exp_config.py

@staticmethod
def load_config(output_dir: Path) -> MlSpacesExpConfig:
    config_path = output_dir / "experiment_config.pkl"
    with open(config_path, "rb") as f:
        config = pickle.load(f)

    log.info(f"Loaded experiment configuration from {output_dir}")
    return config

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/base_pick_config.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)

    try:
        self.policy_config = self._init_policy_config()
    except RuntimeError as e:
        # Check if this is a CUDA/GPU-related error
        error_msg = str(e)
        if "NVIDIA" in error_msg or "CUDA" in error_msg or "GPU" in error_msg:
            # No GPU available - this is expected on manager nodes that just coordinate jobs
            # Policy config will be initialized later on worker nodes that have GPUs
            print(
                f"Warning: Skipping policy config initialization due to missing GPU: {error_msg}"
            )
            self.policy_config = None  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore
        else:
            raise

    # Auto-create profiler instance if profiling is enabled
    if self.profile and self.profiler is None:
        self.profiler = Profiler()

save_config

save_config(output_dir: Path | str | None = None) -> None

Source code in molmo_spaces/configs/abstract_exp_config.py

def save_config(self, output_dir: Path | str | None = None) -> None:
    if output_dir is None:
        output_dir = self.output_dir

    output_dir = Path(output_dir)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_dir.mkdir(parents=True, exist_ok=True)

    # TODO(wilbert): maybe we should save to another format as well. If using pickle, the
    # context of the module is also included, so to import the pkl outside of the project (like
    # to just validate it outside or reuse it) we would get import errors
    config_path = output_dir / f"experiment_config_{timestamp}.pkl"
    with open(config_path, "wb") as f:
        pickle.dump(self, f)

    log.info(f"Saved experiment configuration to {output_dir}")

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

camera_configs

Classes:

Name	Description
BimanualYamCameraSystem	Camera system for bimanual YAM robot.
CameraConfig	Base specification for a single camera.
CameraSystemConfig	Complete camera system configuration.
EvalExocentricCameraConfig	Eval exocentric camera whose level-0 pose is derived from a shoulder mount.
EvalRobotMountedCameraConfig	Robot-mounted camera with additional intrinsics perturbation for eval.
FisheyeImpl	Which implementation renders a warped camera's image; see
FisheyeMjcfCameraConfig	MJCF camera carrying the parameters FisheyeImpl.CUBEMAP needs.
FixedExocentricCameraConfig	Fixed external camera at a specific world position.
FrankaDroidCameraSystem	Camera system for Franka with DROID-style fixed cameras.
FrankaEasyRandomizedDroidCameraSystem	Camera system for Franka DROID system with wrist cam (ZED mini) and 2 randomized exo cams (ZED 2/ZED 2i).
FrankaEvalCameraSystem	Unified Franka eval camera system with progressive perturbation (0-100 level).
FrankaGoProD405D455CameraSystem	Camera system for Franka with GoPro and D405 analogue cameras with noise.
FrankaGoProD405RandomizedCameraSystem	Camera system for Franka with D405 wrist cam and 2 randomized GoPro exo cams.
FrankaOmniPurposeCameraSystem	Camera system for Franka DROID system with wrist cam (ZED mini), droid-alike left shoulder cam,
FrankaRandomizedD405D455CameraSystem	Camera system for Franka pick-and-place tasks with wrist cam and 2 randomized exo cams.
FrankaRandomizedDroidCameraSystem	Camera system for Franka DROID system with wrist cam (ZED mini) and 2 randomized exo cams (ZED 2/ZED 2i).
FrankaRobotiq2f85CameraSystem	Camera system for Franka with Robotiq 2f85 wrist cam and 2 randomized GoPro exo cams.
G1CameraSystem	G1 humanoid: head fisheye + right wrist camera, matching g1_molmo's
I2rtYamCameraSystem	Camera system for i2rt YAM robot.
MjcfCameraConfig	Camera defined in the MJCF file.
RBY1GoProD455CameraSystem	Camera system for RBY1 with GoPro head camera and D455 wrist cameras.
RBY1MjcfCameraSystem	Camera system using RBY1's built-in MJCF cameras.
RandomizedExocentricCameraConfig	Randomized external camera positioned around a workspace center.
RobotMountedCameraConfig	Camera dynamically mounted to a robot body.

Attributes:

Name	Type	Description
AllCameraSystems	TypeAlias
AllCameraTypes	TypeAlias
T
Triple	TypeAlias
logger

AllCameraSystems module-attribute

AllCameraSystems: TypeAlias = RBY1MjcfCameraSystem | RBY1GoProD455CameraSystem | G1CameraSystem | FrankaRandomizedD405D455CameraSystem | FrankaEasyRandomizedDroidCameraSystem | FrankaDroidCameraSystem | FrankaOmniPurposeCameraSystem | FrankaRandomizedDroidCameraSystem | FrankaGoProD405D455CameraSystem | FrankaGoProD405RandomizedCameraSystem | FrankaRobotiq2f85CameraSystem | FrankaEvalCameraSystem | I2rtYamCameraSystem | BimanualYamCameraSystem | FrankaEvalCameraSystem

AllCameraTypes module-attribute

AllCameraTypes: TypeAlias = FisheyeMjcfCameraConfig | MjcfCameraConfig | RobotMountedCameraConfig | FixedExocentricCameraConfig | RandomizedExocentricCameraConfig | EvalRobotMountedCameraConfig | EvalExocentricCameraConfig

T module-attribute

T = TypeVar('T')

Triple module-attribute

Triple: TypeAlias = tuple[T, T, T]

logger module-attribute

logger = logging.getLogger(__name__)

BimanualYamCameraSystem

Bases: CameraSystemConfig

Camera system for bimanual YAM robot.

Includes wrist cameras on both arms (defined in yam.xml MJCF) and a robot-mounted exo camera positioned to see both arms and the workspace between them.

Note: Camera offset z must account for the base platform height (0.7m). The exo camera is positioned slightly back and higher to capture both arms.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='left_wrist_camera', mjcf_name='wrist_camera', robot_namespace='robot_0/left_', fov=58.0), MjcfCameraConfig(name='right_wrist_camera', mjcf_name='wrist_camera', robot_namespace='robot_0/right_', fov=58.0), RobotMountedCameraConfig(name='exo_camera', reference_body_names=['robot_0/base', 'robot_0/left_arm'], camera_offset=[0.0, 0.0, 1.56], camera_quaternion=[0.687, 0.1675, -0.1675, -0.687], fov=58.0, visibility_constraints={'__task_objects__': 0.001})]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 368)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

CameraConfig

Bases: Config, ABC

Base specification for a single camera.

Each camera spec defines how one camera should be created and configured. Subclasses implement different camera types (MJCF, robot-mounted, exocentric).

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
fisheye_impl	FisheyeImpl
fov	float | None
is_warped	bool
model_config
name	str
record_depth	bool
reset_cadence	CameraResetCadence
skip_erosion	bool
visibility_constraints	dict[str, float] | None

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

fov class-attribute instance-attribute

fov: float | None = None

is_warped class-attribute instance-attribute

is_warped: bool = False

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

record_depth class-attribute instance-attribute

record_depth: bool = False

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

CameraSystemConfig

Bases: Config

Complete camera system configuration.

Defines all cameras that should be set up in the environment, along with shared settings like resolution.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = []

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 480)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

EvalExocentricCameraConfig

Bases: FixedExocentricCameraConfig

Eval exocentric camera whose level-0 pose is derived from a shoulder mount.

At runtime the reference body pose is resolved into world-frame pos/forward/up, decomposed into spherical coordinates relative to the workspace center, perturbed according to the spherical noise params, and then placed via the normal fixed-exocentric path.

pos, forward, up default to None here (overriding the required parent fields) because they are computed at runtime.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
azimuth_range	tuple[float, float] | None
camera_offset	list[float]
camera_quaternion	list[float]
distance_range	tuple[float, float] | None
fisheye_impl	FisheyeImpl
forward	list[float] | None
fov	float | None
fov_range	tuple[float, float] | None
height_range	tuple[float, float] | None
is_warped	bool
lookat_noise_range	tuple[float, float] | None
max_placement_attempts	int
model_config
name	str
orientation_noise_degrees	float | Triple[float] | None
pos	list[float] | None
pos_noise_range	tuple[float, float] | tuple[Triple[float], Triple[float]] | None
record_depth	bool
reference_body_names	list[str]
reset_cadence	CameraResetCadence
skip_erosion	bool
up	list[float] | None
visibility_constraints	dict[str, float] | None
workspace_center_weight	float | None

azimuth_range class-attribute instance-attribute

azimuth_range: tuple[float, float] | None = None

camera_offset class-attribute instance-attribute

camera_offset: list[float] = [0.1, 0.57, 0.66]

camera_quaternion class-attribute instance-attribute

camera_quaternion: list[float] = [-0.3633, -0.1241, 0.4263, 0.8191]

distance_range class-attribute instance-attribute

distance_range: tuple[float, float] | None = None

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

forward class-attribute instance-attribute

forward: list[float] | None = None

fov class-attribute instance-attribute

fov: float | None = None

fov_range class-attribute instance-attribute

fov_range: tuple[float, float] | None = None

height_range class-attribute instance-attribute

height_range: tuple[float, float] | None = None

is_warped class-attribute instance-attribute

is_warped: bool = False

lookat_noise_range class-attribute instance-attribute

lookat_noise_range: tuple[float, float] | None = None

max_placement_attempts class-attribute instance-attribute

max_placement_attempts: int = 50

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

orientation_noise_degrees class-attribute instance-attribute

orientation_noise_degrees: float | Triple[float] | None = None

pos class-attribute instance-attribute

pos: list[float] | None = None

pos_noise_range class-attribute instance-attribute

pos_noise_range: tuple[float, float] | tuple[Triple[float], Triple[float]] | None = None

record_depth class-attribute instance-attribute

record_depth: bool = False

reference_body_names class-attribute instance-attribute

reference_body_names: list[str] = ['robot_0/fr3_link0']

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

up class-attribute instance-attribute

up: list[float] | None = None

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

workspace_center_weight class-attribute instance-attribute

workspace_center_weight: float | None = None

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

EvalRobotMountedCameraConfig

Bases: RobotMountedCameraConfig

Robot-mounted camera with additional intrinsics perturbation for eval.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_offset	list[float]
camera_quaternion	list[float] | None
fisheye_impl	FisheyeImpl
fov	float | None
fov_noise_degrees	tuple[float, float] | None
is_warped	bool
lookat_noise_range	tuple[float, float] | None
lookat_offset	list[float]
model_config
name	str
orientation_noise_degrees	float | None
pos_noise_range	tuple[float, float] | None
record_depth	bool
reference_body_names	list[str]
reset_cadence	CameraResetCadence
skip_erosion	bool
up_axis	str
visibility_constraints	dict[str, float] | None

camera_offset class-attribute instance-attribute

camera_offset: list[float] = [0.1, 0.0, -0.15]

camera_quaternion class-attribute instance-attribute

camera_quaternion: list[float] | None = None

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

fov class-attribute instance-attribute

fov: float | None = None

fov_noise_degrees class-attribute instance-attribute

fov_noise_degrees: tuple[float, float] | None = None

is_warped class-attribute instance-attribute

is_warped: bool = False

lookat_noise_range class-attribute instance-attribute

lookat_noise_range: tuple[float, float] | None = None

lookat_offset class-attribute instance-attribute

lookat_offset: list[float] = [0.0, 0.0, 0.08]

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

orientation_noise_degrees class-attribute instance-attribute

orientation_noise_degrees: float | None = None

pos_noise_range class-attribute instance-attribute

pos_noise_range: tuple[float, float] | None = None

record_depth class-attribute instance-attribute

record_depth: bool = False

reference_body_names instance-attribute

reference_body_names: list[str]

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

up_axis class-attribute instance-attribute

up_axis: str = 'z'

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FisheyeImpl

Bases: StrEnum

Which implementation renders a warped camera's image; see CameraConfig.fisheye_impl. Ignored unless the camera sets is_warped.

CUBEMAP utils/fisheye_cubemap.py. Composites five wide tile cameras through an OpenCV fisheye model calibrated on the real lens. Needs the five tile cameras present in the MJCF, which is its only real cost. Use this for any new warped camera. The G1 head is the only one today; see FisheyeMjcfCameraConfig. WARPING DEPRECATED -- utils/fisheye_warping.py. Post-distorts one pinhole render with a radial k1..k4 model. Do not use for new cameras.

At matched lens, FOV and render budget CUBEMAP is 5.1x sharper (variance of Laplacian 5476 vs 1070), marginally faster (5.90 vs 6.24 ms/frame), and reaches the G1 head lens's 72.8 deg half-FOV, which WARPING's k1..k4 polynomial caps at 68.26 deg (mlspaces_tests/component_tests/ compare_fisheye_renderers.py). WARPING stays the default only so existing configs keep their recorded behaviour; nothing renders through it today.

Attributes:

Name	Type	Description
CUBEMAP
WARPING

CUBEMAP class-attribute instance-attribute

CUBEMAP = 'cubemap'

WARPING class-attribute instance-attribute

WARPING = 'warping'

FisheyeMjcfCameraConfig

Bases: MjcfCameraConfig

MJCF camera carrying the parameters FisheyeImpl.CUBEMAP needs.

Only worth using with fisheye_impl=CUBEMAP -- the WARPING default reads none of these fields. The G1 head is the one camera wide enough to need it; see G1CameraSystem.

fov stays the pinhole camera's FOV and tile_fov the tile cameras', so both render paths take their FOV from this config and neither drifts.

Methods:

Name	Description
cubemap_renderer_kwargs	Every FisheyeRenderer argument except the MuJoCo model, so callers
from_dict
load_from_json
save_to_json
tile_camera_names	Fully-qualified MJCF names of the five tile cameras ("cubemap" backend).
to_dict
to_json

Attributes:

Name	Type	Description
distortion_noise	float | None
fisheye_D	list[float]
fisheye_K	list[list[float]]
fisheye_image_size	tuple[int, int]
fisheye_impl	FisheyeImpl
fov	float | None
fov_noise_degrees	tuple[float, float] | None
fov_noise_focal_divisor	float
geomgroup_overrides	dict[int, int] | None
is_warped	bool
mjcf_name	str
model_config
name	str
noise_model	CameraNoiseModel
orientation_noise_degrees	float | Triple[float] | None
pos_noise_range	tuple[float, float] | tuple[Triple[float], Triple[float]] | None
record_depth	bool
render_via_mjcf	bool
reset_cadence	CameraResetCadence
robot_namespace	str | None
skip_erosion	bool
tile_fov	float
tile_size	int
tile_suffixes	tuple[str, str, str, str, str]
visibility_constraints	dict[str, float] | None
weight_power	float

distortion_noise class-attribute instance-attribute

distortion_noise: float | None = None

fisheye_D class-attribute instance-attribute

fisheye_D: list[float] = [-0.02559442829261663, 0.008371943913215045, -0.006921566406199126, 0.0010132813066123071]

fisheye_K class-attribute instance-attribute

fisheye_K: list[list[float]] = [[801.6382129934864, 0.0, 976.1246839545557], [0.0, 802.1081824931498, 542.7122090223202], [0.0, 0.0, 1.0]]

fisheye_image_size class-attribute instance-attribute

fisheye_image_size: tuple[int, int] = (1920, 1080)

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

fov class-attribute instance-attribute

fov: float | None = None

fov_noise_degrees class-attribute instance-attribute

fov_noise_degrees: tuple[float, float] | None = None

fov_noise_focal_divisor class-attribute instance-attribute

fov_noise_focal_divisor: float = 90.0

geomgroup_overrides class-attribute instance-attribute

geomgroup_overrides: dict[int, int] | None = None

is_warped class-attribute instance-attribute

is_warped: bool = False

mjcf_name instance-attribute

mjcf_name: str

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

noise_model class-attribute instance-attribute

noise_model: CameraNoiseModel = CameraNoiseModel.CAMERA_LOCAL_EULER

orientation_noise_degrees class-attribute instance-attribute

orientation_noise_degrees: float | Triple[float] | None = None

pos_noise_range class-attribute instance-attribute

pos_noise_range: tuple[float, float] | tuple[Triple[float], Triple[float]] | None = None

record_depth class-attribute instance-attribute

record_depth: bool = False

render_via_mjcf class-attribute instance-attribute

render_via_mjcf: bool = False

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

robot_namespace class-attribute instance-attribute

robot_namespace: str | None = None

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

tile_fov class-attribute instance-attribute

tile_fov: float = 100.0

tile_size class-attribute instance-attribute

tile_size: int = 512

tile_suffixes class-attribute instance-attribute

tile_suffixes: tuple[str, str, str, str, str] = ('center', 'up', 'down', 'left', 'right')

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

weight_power class-attribute instance-attribute

weight_power: float = 4.0

cubemap_renderer_kwargs

cubemap_renderer_kwargs(output_h: int, output_w: int) -> dict

Every FisheyeRenderer argument except the MuJoCo model, so callers construct one straight from config rather than restating its parameters.

Output size stays a call argument: it is the resolution the caller wants this frame (CameraSystemConfig.img_resolution), not a property of the lens.

Source code in molmo_spaces/configs/camera_configs.py

def cubemap_renderer_kwargs(self, output_h: int, output_w: int) -> dict:
    """Every FisheyeRenderer argument except the MuJoCo model, so callers
    construct one straight from config rather than restating its parameters.

    Output size stays a call argument: it is the resolution the caller wants
    this frame (CameraSystemConfig.img_resolution), not a property of the
    lens.
    """
    return {
        "tile_cam_names": self.tile_camera_names(),
        "K": self.fisheye_K,
        "D": self.fisheye_D,
        "image_size": self.fisheye_image_size,
        "tile_size": self.tile_size,
        "tile_fovy": self.tile_fov,
        "weight_power": self.weight_power,
        "output_h": output_h,
        "output_w": output_w,
    }

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

tile_camera_names

tile_camera_names() -> list[str]

Fully-qualified MJCF names of the five tile cameras ("cubemap" backend).

Source code in molmo_spaces/configs/camera_configs.py

def tile_camera_names(self) -> list[str]:
    """Fully-qualified MJCF names of the five tile cameras ("cubemap" backend)."""
    prefix = self.robot_namespace or ""
    return [f"{prefix}{self.mjcf_name}_tile_{s}" for s in self.tile_suffixes]

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FixedExocentricCameraConfig

Bases: CameraConfig

Fixed external camera at a specific world position.

Useful for consistent third-person views, overhead cameras, or monitoring positions. Can optionally add small amounts of noise for data augmentation.

TODO: should this also have a quaternion option? was figuring this would be most useful for fixed eval episodes

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
fisheye_impl	FisheyeImpl
forward	list[float]
fov	float | None
is_warped	bool
model_config
name	str
orientation_noise_degrees	float | Triple[float] | None
pos	list[float]
pos_noise_range	tuple[float, float] | tuple[Triple[float], Triple[float]] | None
record_depth	bool
reset_cadence	CameraResetCadence
skip_erosion	bool
up	list[float]
visibility_constraints	dict[str, float] | None

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

forward instance-attribute

forward: list[float]

fov class-attribute instance-attribute

fov: float | None = None

is_warped class-attribute instance-attribute

is_warped: bool = False

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

orientation_noise_degrees class-attribute instance-attribute

orientation_noise_degrees: float | Triple[float] | None = None

pos instance-attribute

pos: list[float]

pos_noise_range class-attribute instance-attribute

pos_noise_range: tuple[float, float] | tuple[Triple[float], Triple[float]] | None = None

record_depth class-attribute instance-attribute

record_depth: bool = False

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

up instance-attribute

up: list[float]

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaDroidCameraSystem

Bases: CameraSystemConfig

Camera system for Franka with DROID-style fixed cameras.

Uses wrist camera plus DROID-style exocentric camera mounted to robot base. All cameras are deterministic (no noise) for consistent, reproducible viewpoints. This matches the behavior of the old cameras_fixed_droid=True setting.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='gripper/wrist_camera', robot_namespace='robot_0/', fov=56.74), RobotMountedCameraConfig(name='exo_camera_1', reference_body_names=['robot_0/fr3_link0'], camera_offset=[0.1, 0.57, 0.66], camera_quaternion=[-0.3633, -0.1241, 0.4263, 0.8191], fov=71.0, visibility_constraints={'__task_objects__': 0.001})]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 368)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaEasyRandomizedDroidCameraSystem

Bases: CameraSystemConfig

Camera system for Franka DROID system with wrist cam (ZED mini) and 2 randomized exo cams (ZED 2/ZED 2i).

Uses workspace center from task sampler for dynamic placement. The task sampler should implement get_workspace_center() and resolve_visibility_object() to provide runtime information without modifying the camera config.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='gripper/wrist_camera', robot_namespace='robot_0/', fov=52.0, fov_noise_degrees=(-4.0, 4.0), pos_noise_range=((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), orientation_noise_degrees=(8.0, 4.0, 4.0), record_depth=True), RobotMountedCameraConfig(name='exo_camera_1', reference_body_names=['robot_0/fr3_link0'], camera_offset=[0.1, 0.57, 0.66], camera_quaternion=[-0.3633, -0.1241, 0.4263, 0.8191], fov=71.0, pos_noise_range=(-0.05, 0.05), orientation_noise_degrees=8.0, visibility_constraints={'__task_objects__': 0.001})]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 368)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaEvalCameraSystem

Bases: CameraSystemConfig

Unified Franka eval camera system with progressive perturbation (0-100 level).

Cameras: - 1 wrist camera (FrankaDroid-like) - 1 ZED2-like exocentric cameras (stored pose from episode + perturbation)

The cameras list holds only calibrated (level 0) specs with no randomization ranges. All randomization ranges are in per-reference level, per-camera dicts. Level scaling is applied by apply_eval_camera_randomization_level() in eval_camera_randomization_utils.py using an N-piece linear curve (e.g. 0→low→high→100 for N=3).

Exo camera pos/forward/up are placeholders; the runtime loads stored poses from episode specs.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config
ref_level_ranges	list[tuple[float, dict[str, dict[str, float | tuple[float, ...] | tuple[tuple[float, ...], ...]]]]]

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='gripper/wrist_camera', robot_namespace='robot_0/', fov=52.0), EvalExocentricCameraConfig(name='exo_camera_1', fov=71.0, visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001})]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 368)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

ref_level_ranges class-attribute

ref_level_ranges: list[tuple[float, dict[str, dict[str, float | tuple[float, ...] | tuple[tuple[float, ...], ...]]]]] = [(0.0, {'wrist_camera': {'pos_noise_range': ((0.0, 0.0, 0.0), (0.0, 0.0, 0.0)), 'orientation_noise_degrees': (0.0, 0.0, 0.0), 'fov_noise_degrees': (0.0, 0.0)}, 'exo_camera_1': {'azimuth_range': (0.0, 0.0), 'distance_range': (0.0, 0.0), 'height_range': (0.0, 0.0), 'workspace_center_weight': 0.0, 'lookat_noise_range': (0.0, 0.0), 'fov_range': (71.0, 71.0)}}), (10.0, {'wrist_camera': {'pos_noise_range': ((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), 'orientation_noise_degrees': (8.0, 4.0, 4.0), 'fov_noise_degrees': (-4.0, 4.0)}, 'exo_camera_1': {'azimuth_range': (-np.pi / 4, np.pi / 4), 'distance_range': (-0.05, 0.05), 'height_range': (-0.05, 0.05), 'workspace_center_weight': 1.0, 'lookat_noise_range': (-0.01, 0.01), 'fov_range': (71.0, 71.0)}}), (40.0, {'wrist_camera': {'pos_noise_range': ((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), 'orientation_noise_degrees': (8.0, 4.0, 4.0), 'fov_noise_degrees': (-4.0, 4.0)}, 'exo_camera_1': {'azimuth_range': (-np.pi / 2, np.pi / 2), 'distance_range': (-0.5, 0.5), 'height_range': (-0.1, 0.5), 'workspace_center_weight': 1.0, 'lookat_noise_range': (-0.05, 0.05), 'fov_range': (64.0, 72.0)}}), (75.0, {'wrist_camera': {'pos_noise_range': ((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), 'orientation_noise_degrees': (8.0, 4.0, 4.0), 'fov_noise_degrees': (-4.0, 4.0)}, 'exo_camera_1': dict(azimuth_range=(-np.pi, np.pi), distance_range=(-0.5, 1.0), height_range=(-0.2, 0.7), workspace_center_weight=1.0, lookat_noise_range=(-0.1, 0.1), fov_range=(64.0, 72.0))}), (100.0, {'wrist_camera': {'pos_noise_range': ((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), 'orientation_noise_degrees': (8.0, 4.0, 4.0), 'fov_noise_degrees': (-4.0, 4.0)}, 'exo_camera_1': dict(azimuth_range=(-np.pi, np.pi), distance_range=(-0.5, 1.5), height_range=(-0.3, 0.8), workspace_center_weight=1.0, lookat_noise_range=(-0.15, 0.15), fov_range=(64.0, 78.0))})]

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaGoProD405D455CameraSystem

Bases: CameraSystemConfig

Camera system for Franka with GoPro and D405 analogue cameras with noise.

Uses: - D405 analogue wrist camera: VFOV=58°, resolution 640x480, with position and orientation noise - 455 analogue exo camera: VFOV=58°, resolution 640x480, with position and orientation noise but around droid shoulder - GoPro analogue exo camera: VFOV=139°, resolution 640x480, with position and orientation noise

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='gripper/wrist_camera', robot_namespace='robot_0/', fov=58.0, record_depth=True, pos_noise_range=(-0.01, 0.01), orientation_noise_degrees=2.0), RobotMountedCameraConfig(name='exo_camera_1', reference_body_names=['robot_0/fr3_link0'], camera_offset=[0.1, 0.57, 0.66], camera_quaternion=[-0.3633, -0.1241, 0.4263, 0.8191], fov=58.0, is_warped=False, pos_noise_range=(-0.1, 0.1), orientation_noise_degrees=3.0, visibility_constraints={'__task_objects__': 0.001}), RandomizedExocentricCameraConfig(name='exo_camera_2', distance_range=(0.2, 0.5), height_range=(0.1, 0.6), azimuth_range=(0, 2 * np.pi), fov=139.0, is_warped=False, lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, max_placement_attempts=20, allow_relaxed_constraints=False)]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 480)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaGoProD405RandomizedCameraSystem

Bases: CameraSystemConfig

Camera system for Franka with D405 wrist cam and 2 randomized GoPro exo cams.

Uses: - D405 analogue wrist camera: VFOV=58°, resolution 640x480, with position and orientation noise - Two randomized GoPro exo cameras: VFOV=139°, resolution 640x480, with visibility constraints

Workspace center sourced from task sampler, exo cameras positioned to maximize visibility of pickup object and gripper.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='wrist_cam', robot_namespace='robot_0/', fov=58.0, record_depth=True, pos_noise_range=(-0.01, 0.01), orientation_noise_degrees=2.0), RandomizedExocentricCameraConfig(name='exo_camera_1', distance_range=(0.4, 1.0), height_range=(0.4, 0.8), azimuth_range=(0, 2 * np.pi), fov=139.0, is_warped=False, lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, max_placement_attempts=20, allow_relaxed_constraints=False), RandomizedExocentricCameraConfig(name='exo_camera_2', distance_range=(0.4, 1.0), height_range=(0.4, 0.8), azimuth_range=(0, 2 * np.pi), fov=139.0, is_warped=False, lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, max_placement_attempts=20, allow_relaxed_constraints=False)]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 480)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaOmniPurposeCameraSystem

Bases: CameraSystemConfig

Camera system for Franka DROID system with wrist cam (ZED mini), droid-alike left shoulder cam, 2 randomized Zed2 cams, and 1 randomized GoPro cam. Intended such that data with this camera system can be used for a wide variety of purposes and maximally consistent ablations.

Uses workspace center from task sampler for dynamic placement. The task sampler should implement get_workspace_center() and resolve_visibility_object() to provide runtime information without modifying the camera config.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera_zed_mini', mjcf_name='gripper/wrist_camera', robot_namespace='robot_0/', fov=52.0, fov_noise_degrees=(-4.0, 4.0), pos_noise_range=((-0.015, -0.005, -0.02), (0.015, 0.005, 0.02)), orientation_noise_degrees=(8.0, 4.0, 4.0), record_depth=True), RobotMountedCameraConfig(name='droid_shoulder_light_randomization', reference_body_names=['robot_0/fr3_link0'], camera_offset=[0.1, 0.57, 0.66], camera_quaternion=[-0.3633, -0.1241, 0.4263, 0.8191], fov=71.0, pos_noise_range=(-0.05, 0.05), orientation_noise_degrees=8.0, visibility_constraints={'__task_objects__': 0.001}), RandomizedExocentricCameraConfig(name='randomized_zed2_analogue_1', distance_range=(0.2, 0.8), height_range=(0.05, 0.6), azimuth_range=(0, 2 * np.pi), fov_range=(64, 72), lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, max_placement_attempts=20, allow_relaxed_constraints=False), RandomizedExocentricCameraConfig(name='randomized_zed2_analogue_2', distance_range=(0.2, 0.8), height_range=(0.05, 0.6), azimuth_range=(0, 2 * np.pi), fov_range=(64, 72), lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, max_placement_attempts=20, allow_relaxed_constraints=False), RandomizedExocentricCameraConfig(name='randomized_gopro_analogue_1', distance_range=(0.2, 0.5), height_range=(0.1, 0.6), azimuth_range=(0, 2 * np.pi), fov_range=(137, 140), is_warped=False, lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, max_placement_attempts=20, allow_relaxed_constraints=False)]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 368)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaRandomizedD405D455CameraSystem

Bases: CameraSystemConfig

Camera system for Franka pick-and-place tasks with wrist cam and 2 randomized exo cams.

Uses workspace center from task sampler for dynamic placement. The task sampler should implement get_workspace_center() and resolve_visibility_object() to provide runtime information without modifying the camera config.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='wrist_cam', robot_namespace='robot_0/', fov=58.0, fov_noise_degrees=(-10.0, 10.0), pos_noise_range=(-0.015, 0.015), orientation_noise_degrees=8.0), RandomizedExocentricCameraConfig(name='exo_camera_1', distance_range=(0.2, 0.8), height_range=(0.4, 0.8), azimuth_range=(0, 2 * np.pi), fov_range=(50, 90), lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, allow_relaxed_constraints=False), RandomizedExocentricCameraConfig(name='exo_camera_2', distance_range=(0.2, 0.8), height_range=(0.4, 0.8), azimuth_range=(0, 2 * np.pi), fov_range=(50, 90), lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, allow_relaxed_constraints=False)]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 368)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaRandomizedDroidCameraSystem

Bases: CameraSystemConfig

Camera system for Franka DROID system with wrist cam (ZED mini) and 2 randomized exo cams (ZED 2/ZED 2i).

Uses workspace center from task sampler for dynamic placement. The task sampler should implement get_workspace_center() and resolve_visibility_object() to provide runtime information without modifying the camera config.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='gripper/wrist_camera', robot_namespace='robot_0/', fov=52.0, fov_noise_degrees=(-4.0, 4.0), pos_noise_range=((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), orientation_noise_degrees=(8.0, 4.0, 4.0)), RandomizedExocentricCameraConfig(name='exo_camera_1', distance_range=(0.2, 0.8), height_range=(0.05, 0.6), azimuth_range=(0, 2 * np.pi), fov_range=(64, 72), lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, allow_relaxed_constraints=False), RandomizedExocentricCameraConfig(name='exo_camera_2', distance_range=(0.2, 0.8), height_range=(0.05, 0.6), azimuth_range=(0, 2 * np.pi), fov_range=(64, 72), lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, allow_relaxed_constraints=False), RandomizedExocentricCameraConfig(name='exo_camera_3', distance_range=(0.2, 0.5), height_range=(0.1, 0.6), azimuth_range=(0, 2 * np.pi), fov_range=(137, 140), is_warped=False, lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__task_objects__': 0.0001, '__gripper__': 0.0001}, max_placement_attempts=20, allow_relaxed_constraints=False)]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 368)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaRobotiq2f85CameraSystem

Bases: CameraSystemConfig

Camera system for Franka with Robotiq 2f85 wrist cam and 2 randomized GoPro exo cams.

Uses: - Robotiq 2f85 wrist camera: VFOV=56.74°, resolution 1280x720, with position and orientation noise - Two randomized GoPro exo cameras: VFOV=139°, resolution 640x480, with visibility constraints

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='wrist_camera', robot_namespace='robot_0/'), RandomizedExocentricCameraConfig(name='exo_camera_1', distance_range=(0.4, 1.0), height_range=(0.4, 0.8), azimuth_range=(0, 2 * np.pi), fov=139.0, is_warped=False, lookat_noise_range=(-0.1, 0.1), visibility_constraints={'__pickup_object__': 0.001}, max_placement_attempts=200, allow_relaxed_constraints=True)]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 480)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

G1CameraSystem

Bases: CameraSystemConfig

G1 humanoid: head fisheye + right wrist camera, matching g1_molmo's camera setup (its bowl config and the base FOVs in g1_dex.xml). head_camera owns every fisheye parameter, including the lens calibration; CameraManager builds the cubemap FisheyeRenderer from it on request. Both cameras set render_via_mjcf, so CPUMujocoEnv.render_rgb_frame draws them through their MJCF camera ids -- the head as its cubemap composite, not the raw head_pov pinhole.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[AllCameraTypes]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[AllCameraTypes] = [FisheyeMjcfCameraConfig(name='head_camera', mjcf_name='head_pov', robot_namespace='robot_0/', fov=None, is_warped=True, fisheye_impl=FisheyeImpl.CUBEMAP, render_via_mjcf=True, geomgroup_overrides={5: 0}, tile_fov=100.0, tile_size=512, noise_model=CameraNoiseModel.BODY_FRAME_AXIS_ANGLE, reset_cadence=CameraResetCadence.EPISODE, orientation_noise_degrees=2.0, pos_noise_range=(-0.01, 0.01), distortion_noise=0.2, fov_noise_focal_divisor=90.0, fov_noise_degrees=(-2.0, 2.0)), MjcfCameraConfig(name='wrist_camera', mjcf_name='right_wrist_camera', robot_namespace='robot_0/', fov=None, render_via_mjcf=True, geomgroup_overrides={1: 0, 5: 1}, record_depth=False, noise_model=CameraNoiseModel.BODY_FRAME_AXIS_ANGLE, reset_cadence=CameraResetCadence.EPISODE, orientation_noise_degrees=2.0, pos_noise_range=(-0.01, 0.01), fov_noise_degrees=(-2.0, 2.0))]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (384, 224)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

I2rtYamCameraSystem

Bases: CameraSystemConfig

Camera system for i2rt YAM robot.

Uses robot-mounted exo camera since YAM doesn't have built-in MJCF cameras. The exo camera is mounted relative to the robot base (mocap body at ground level).

Note: Camera offset z must account for the base platform height (0.7m). To achieve similar viewing angle as Franka DROID (camera at ~1.24m total height), we use z offset = 0.7 (platform) + 0.5 (above platform) = 1.2m

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='wrist_camera', mjcf_name='wrist_camera', robot_namespace='robot_0/'), RobotMountedCameraConfig(name='exo_camera_1', reference_body_names=['robot_0/base', 'robot_0/arm'], camera_offset=[0.1, 0.5, 1.2], camera_quaternion=[-0.3633, -0.1241, 0.4263, 0.8191], fov=71.0, visibility_constraints={'__task_objects__': 0.001})]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 480)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

MjcfCameraConfig

Bases: CameraConfig

Camera defined in the MJCF file.

This references a camera that already exists in the scene MJCF or robot MJCF. Useful for cameras with fixed mounting in robot models.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
fisheye_impl	FisheyeImpl
fov	float | None
fov_noise_degrees	tuple[float, float] | None
geomgroup_overrides	dict[int, int] | None
is_warped	bool
mjcf_name	str
model_config
name	str
noise_model	CameraNoiseModel
orientation_noise_degrees	float | Triple[float] | None
pos_noise_range	tuple[float, float] | tuple[Triple[float], Triple[float]] | None
record_depth	bool
render_via_mjcf	bool
reset_cadence	CameraResetCadence
robot_namespace	str | None
skip_erosion	bool
visibility_constraints	dict[str, float] | None

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

fov class-attribute instance-attribute

fov: float | None = None

fov_noise_degrees class-attribute instance-attribute

fov_noise_degrees: tuple[float, float] | None = None

geomgroup_overrides class-attribute instance-attribute

geomgroup_overrides: dict[int, int] | None = None

is_warped class-attribute instance-attribute

is_warped: bool = False

mjcf_name instance-attribute

mjcf_name: str

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

noise_model class-attribute instance-attribute

noise_model: CameraNoiseModel = CameraNoiseModel.CAMERA_LOCAL_EULER

orientation_noise_degrees class-attribute instance-attribute

orientation_noise_degrees: float | Triple[float] | None = None

pos_noise_range class-attribute instance-attribute

pos_noise_range: tuple[float, float] | tuple[Triple[float], Triple[float]] | None = None

record_depth class-attribute instance-attribute

record_depth: bool = False

render_via_mjcf class-attribute instance-attribute

render_via_mjcf: bool = False

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

robot_namespace class-attribute instance-attribute

robot_namespace: str | None = None

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RBY1GoProD455CameraSystem

Bases: CameraSystemConfig

Camera system for RBY1 with GoPro head camera and D455 wrist cameras.

Renders at 1024x576 (16:9) to accommodate both: - Head camera: GoPro analogue (4:3, crop to 768x576 in post-processing) - Wrist cameras: D455 analogue (16:9, use full frame)

All cameras include randomization for sim-to-real transfer.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='head_camera', mjcf_name='head_camera', robot_namespace='robot_0/', fov=139.0, fov_noise_degrees=(-3.0, 3.0), pos_noise_range=((-0.01, -0.01, -0.01), (0.01, 0.01, 0.01)), orientation_noise_degrees=(4.0, 4.0, 4.0), skip_erosion=True), MjcfCameraConfig(name='wrist_camera_l', mjcf_name='wrist_camera_l', robot_namespace='robot_0/', fov=58.0, fov_noise_degrees=(-4.0, 4.0), pos_noise_range=((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), orientation_noise_degrees=(8.0, 4.0, 4.0), record_depth=True), MjcfCameraConfig(name='wrist_camera_r', mjcf_name='wrist_camera_r', robot_namespace='robot_0/', fov=58.0, fov_noise_degrees=(-4.0, 4.0), pos_noise_range=((-0.015, -0.005, -0.01), (0.015, 0.005, 0.01)), orientation_noise_degrees=(8.0, 4.0, 4.0), record_depth=True)]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (1024, 576)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RBY1MjcfCameraSystem

Bases: CameraSystemConfig

Camera system using RBY1's built-in MJCF cameras.

Methods:

Name	Description
add_camera	Add a camera specification to the system.
from_dict
get_camera_by_name	Get a camera spec by name.
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
cameras	list[CameraConfig]
img_resolution	tuple[int, int]
model_config

cameras class-attribute instance-attribute

cameras: list[CameraConfig] = [MjcfCameraConfig(name='head_camera', mjcf_name='head_camera', robot_namespace='robot_0/', fov=139.0, skip_erosion=True), MjcfCameraConfig(name='wrist_camera_l', mjcf_name='wrist_camera_l', robot_namespace='robot_0/', record_depth=True), MjcfCameraConfig(name='wrist_camera_r', mjcf_name='wrist_camera_r', robot_namespace='robot_0/', record_depth=True), MjcfCameraConfig(name='camera_follower', mjcf_name='camera_follower', robot_namespace='robot_0/')]

img_resolution class-attribute instance-attribute

img_resolution: tuple[int, int] = (640, 480)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

add_camera

add_camera(camera_spec: CameraConfig) -> None

Add a camera specification to the system.

Source code in molmo_spaces/configs/camera_configs.py

def add_camera(self, camera_spec: CameraConfig) -> None:
    """Add a camera specification to the system."""
    self.cameras.append(camera_spec)

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_camera_by_name

get_camera_by_name(name: str) -> CameraConfig | None

Get a camera spec by name.

Source code in molmo_spaces/configs/camera_configs.py

def get_camera_by_name(self, name: str) -> CameraConfig | None:
    """Get a camera spec by name."""
    for camera in self.cameras:
        if camera.name == name:
            return camera
    return None

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RandomizedExocentricCameraConfig

Bases: CameraConfig

Randomized external camera positioned around a workspace center.

Samples camera position within specified ranges around a workspace center. Can use visibility constraints to ensure good views of important objects. CORE ASSUMPTION: workspace center will be sourced from task sampler callback function get_workspace_center you will always be looking at the workspace center (with optional noise).

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
allow_relaxed_constraints	bool
azimuth_range	tuple[float, float]
distance_range	tuple[float, float]
fisheye_impl	FisheyeImpl
fov	float | None
fov_range	tuple[float, float] | None
height_range	tuple[float, float]
is_warped	bool
lookat_noise_range	tuple[float, float] | None
max_placement_attempts	int
model_config
name	str
record_depth	bool
reset_cadence	CameraResetCadence
skip_erosion	bool
visibility_constraints	dict[str, float] | None

allow_relaxed_constraints class-attribute instance-attribute

allow_relaxed_constraints: bool = False

azimuth_range instance-attribute

azimuth_range: tuple[float, float]

distance_range instance-attribute

distance_range: tuple[float, float]

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

fov class-attribute instance-attribute

fov: float | None = None

fov_range class-attribute instance-attribute

fov_range: tuple[float, float] | None = None

height_range instance-attribute

height_range: tuple[float, float]

is_warped class-attribute instance-attribute

is_warped: bool = False

lookat_noise_range class-attribute instance-attribute

lookat_noise_range: tuple[float, float] | None = None

max_placement_attempts class-attribute instance-attribute

max_placement_attempts: int = 100

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

record_depth class-attribute instance-attribute

record_depth: bool = False

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RobotMountedCameraConfig

Bases: CameraConfig

Camera dynamically mounted to a robot body.

Camera follows the specified reference body with configurable offset and orientation. Can use either lookat-based positioning or quaternion-based orientation.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
camera_offset	list[float]
camera_quaternion	list[float] | None
fisheye_impl	FisheyeImpl
fov	float | None
is_warped	bool
lookat_noise_range	tuple[float, float] | None
lookat_offset	list[float]
model_config
name	str
orientation_noise_degrees	float | None
pos_noise_range	tuple[float, float] | None
record_depth	bool
reference_body_names	list[str]
reset_cadence	CameraResetCadence
skip_erosion	bool
up_axis	str
visibility_constraints	dict[str, float] | None

camera_offset class-attribute instance-attribute

camera_offset: list[float] = [0.1, 0.0, -0.15]

camera_quaternion class-attribute instance-attribute

camera_quaternion: list[float] | None = None

fisheye_impl class-attribute instance-attribute

fisheye_impl: FisheyeImpl = FisheyeImpl.WARPING

fov class-attribute instance-attribute

fov: float | None = None

is_warped class-attribute instance-attribute

is_warped: bool = False

lookat_noise_range class-attribute instance-attribute

lookat_noise_range: tuple[float, float] | None = None

lookat_offset class-attribute instance-attribute

lookat_offset: list[float] = [0.0, 0.0, 0.08]

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str

orientation_noise_degrees class-attribute instance-attribute

orientation_noise_degrees: float | None = None

pos_noise_range class-attribute instance-attribute

pos_noise_range: tuple[float, float] | None = None

record_depth class-attribute instance-attribute

record_depth: bool = False

reference_body_names instance-attribute

reference_body_names: list[str]

reset_cadence class-attribute instance-attribute

reset_cadence: CameraResetCadence = CameraResetCadence.SETUP

skip_erosion class-attribute instance-attribute

skip_erosion: bool = False

up_axis class-attribute instance-attribute

up_axis: str = 'z'

visibility_constraints class-attribute instance-attribute

visibility_constraints: dict[str, float] | None = None

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

dummy_config

Classes:

Name	Description
DummyPolicyConfig	Policy config that uses DummyPolicy for testing.

DummyPolicyConfig

Bases: BasePolicyConfig

Policy config that uses DummyPolicy for testing.

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
force_enable_depth	bool	Whether or not to require all cameras to record depth
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'dummy'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/dummy_config.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.policy_cls is None:
        self.policy_cls = DummyPolicy
        self.policy_factory = make_lenient(DummyPolicy)  # pyright: ignore[reportIncompatibleMethodOverride]

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

policy_configs

Policy configuration classes for MolmoSpaces experiments.

Classes:

Name	Description
AStarNavToObjPolicyConfig	Configuration for A* navigation policy (discrete grid-based planner).
BasePolicyConfig	Base configuration for policies.
BrownianMotionPolicyConfig	Policy that applies Gaussian noise increments over noop control, resulting in Brownian motion.
CuroboOpenClosePlannerPolicyConfig
CuroboPickAndPlacePlannerPolicyConfig
DoorOpeningPolicyConfig	Configuration for RBY1 door opening planner policy.
DummyPolicyConfig	Policy config that uses DummyPolicy for testing.
FetchManBasePlannerPolicyConfig	Configuration for FetchManBasePlannerPolicy -- a port of g1_molmo's
NavToObjPlannerPolicyConfig	Base configuration for navigation to object planner policies.
ObjectManipulationPlannerPolicyConfig	Configuration for Franka pick planner policy.
OpenClosePlannerPolicyConfig
PickAndPlaceColorPlannerPolicyConfig
PickAndPlaceNextToPlannerPolicyConfig
PickAndPlacePlannerPolicyConfig
PickPlannerPolicyConfig

AStarNavToObjPolicyConfig

Bases: NavToObjPlannerPolicyConfig

Configuration for A* navigation policy (discrete grid-based planner).

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
downscale	int
force_enable_depth	bool	Whether or not to require all cameras to record depth
map_path	str | None
model_config
num_recovery_steps	int
path_interpolation_density	int
path_max_inter_waypoint_angle	float
path_max_inter_waypoint_dist	float
path_min_dist_to_target_center	float
plan_fail_after_waypoint_steps	int
plan_fail_max_dist_delta	float
plan_max_retries	int
plan_stick_to_original_target	bool
planner_config	AStarPlannerConfig
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
recovery_motion_backward_distance	float
verbose	bool

downscale class-attribute instance-attribute

downscale: int = 5

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

map_path class-attribute instance-attribute

map_path: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_recovery_steps class-attribute instance-attribute

num_recovery_steps: int = 8

path_interpolation_density class-attribute instance-attribute

path_interpolation_density: int = 1

path_max_inter_waypoint_angle class-attribute instance-attribute

path_max_inter_waypoint_angle: float = float(np.deg2rad(10))

path_max_inter_waypoint_dist class-attribute instance-attribute

path_max_inter_waypoint_dist: float = 0.25

path_min_dist_to_target_center class-attribute instance-attribute

path_min_dist_to_target_center: float = 0.8

plan_fail_after_waypoint_steps class-attribute instance-attribute

plan_fail_after_waypoint_steps: int = 10

plan_fail_max_dist_delta class-attribute instance-attribute

plan_fail_max_dist_delta: float = 0.01

plan_max_retries class-attribute instance-attribute

plan_max_retries: int = 3

plan_stick_to_original_target class-attribute instance-attribute

plan_stick_to_original_target: bool = False

planner_config class-attribute instance-attribute

planner_config: AStarPlannerConfig = AStarPlannerConfig()

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

recovery_motion_backward_distance class-attribute instance-attribute

recovery_motion_backward_distance: float = 0.02

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.navigation.astar_planner_policy import (
            AStarSmoothPlannerPolicy,
        )

        self.policy_cls = AStarSmoothPlannerPolicy
        self.policy_factory = AStarSmoothPlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

BasePolicyConfig

Bases: Config

Base configuration for policies.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
force_enable_depth	bool	Whether or not to require all cameras to record depth
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls instance-attribute

policy_cls: type[BasePolicy] | None

policy_factory instance-attribute

policy_factory: PolicyFactory | None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type instance-attribute

policy_type: str

Type of the policy, e.g., "planner", "teleop", "learned", etc.

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

BrownianMotionPolicyConfig

Bases: BasePolicyConfig

Policy that applies Gaussian noise increments over noop control, resulting in Brownian motion.

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
force_enable_depth	bool	Whether or not to require all cameras to record depth
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
std	float

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'dummy'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

std class-attribute instance-attribute

std: float = 0.1

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, _context: Any) -> None:
    super().model_post_init(_context)
    if self.policy_cls is None:
        from molmo_spaces.policy.dummy_policy import BrownianMotionPolicy

        self.policy_cls = BrownianMotionPolicy
        self.policy_factory = make_lenient(BrownianMotionPolicy)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

CuroboOpenClosePlannerPolicyConfig

Bases: OpenClosePlannerPolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
attach_obj	bool
batch_size	int
debug_poses	bool
enable_collision_avoidance	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_horizontal_cost_weight	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_closed_pos	float
gripper_closed_tolerance	float
gripper_empty_threshold	float
gripper_open_duration	float
left_curobo_planner_config	CuroboPlannerConfig | None
left_planner_joint_ranges	dict[str, tuple]
max_batch_plan_attempts	int
max_grasping_timesteps	int
max_height_adjustment_steps	int
max_opening_timesteps	int
max_placement_retries	int
max_planning_reattempts	int
max_retries	int
max_sequential_ik_failures	int
max_settle_steps	int
max_steps_per_waypoint	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
right_curobo_planner_config	CuroboPlannerConfig | None
right_planner_joint_ranges	dict[str, tuple]
server_timeout	float | None
server_urls	list[str]
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
velocity_constraints	dict[str, float]
verbose	bool

attach_obj class-attribute instance-attribute

attach_obj: bool = False

batch_size class-attribute instance-attribute

batch_size: int = 4

debug_poses class-attribute instance-attribute

debug_poses: bool = False

enable_collision_avoidance class-attribute instance-attribute

enable_collision_avoidance: bool = True

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 0.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_horizontal_cost_weight class-attribute instance-attribute

grasp_horizontal_cost_weight: float = 10.0

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = ['droid']

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.05

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 2.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_closed_pos class-attribute instance-attribute

gripper_closed_pos: float = 0.0

gripper_closed_tolerance class-attribute instance-attribute

gripper_closed_tolerance: float = 0.005

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

left_curobo_planner_config class-attribute instance-attribute

left_curobo_planner_config: CuroboPlannerConfig | None = None

left_planner_joint_ranges class-attribute instance-attribute

left_planner_joint_ranges: dict[str, tuple] = {'base': (0, 3), 'left_arm': (3, 10)}

max_batch_plan_attempts class-attribute instance-attribute

max_batch_plan_attempts: int = 4

max_grasping_timesteps class-attribute instance-attribute

max_grasping_timesteps: int = 5

max_height_adjustment_steps class-attribute instance-attribute

max_height_adjustment_steps: int = 10

max_opening_timesteps class-attribute instance-attribute

max_opening_timesteps: int = 5

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_planning_reattempts class-attribute instance-attribute

max_planning_reattempts: int = 2

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

max_settle_steps class-attribute instance-attribute

max_settle_steps: int = 5

max_steps_per_waypoint class-attribute instance-attribute

max_steps_per_waypoint: int = 10

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.2

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.05

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.02

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

right_curobo_planner_config class-attribute instance-attribute

right_curobo_planner_config: CuroboPlannerConfig | None = None

right_planner_joint_ranges class-attribute instance-attribute

right_planner_joint_ranges: dict[str, tuple] = {'base': (0, 3), 'right_arm': (3, 10)}

server_timeout class-attribute instance-attribute

server_timeout: float | None = 300.0

server_urls class-attribute instance-attribute

server_urls: list[str] = ['jupiter-cs-aus-107.reviz.ai2.in:10002']

speed_fast class-attribute instance-attribute

speed_fast: float = 0.08

speed_slow class-attribute instance-attribute

speed_slow: float = 0.04

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

velocity_constraints class-attribute instance-attribute

velocity_constraints: dict[str, float] = {'base': 0.5, 'head': 0.5, 'right_arm': 0.5, 'left_arm': 0.5}

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.object_manipulation.open_close_planner_policy import (
            OpenClosePlannerPolicy,
        )

        self.policy_cls = OpenClosePlannerPolicy
        self.policy_factory = OpenClosePlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

CuroboPickAndPlacePlannerPolicyConfig

Bases: PickAndPlacePlannerPolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
attach_obj	bool
batch_size	int
debug_poses	bool
enable_collision_avoidance	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_closed_pos	float
gripper_closed_tolerance	float
gripper_empty_threshold	float
gripper_open_duration	float
left_curobo_planner_config	CuroboPlannerConfig | None
left_planner_joint_ranges	dict[str, tuple]
max_batch_plan_attempts	int
max_grasping_timesteps	int
max_opening_timesteps	int
max_placement_retries	int
max_planning_reattempts	int
max_retries	int
max_sequential_ik_failures	int
max_settle_steps	int
max_steps_per_waypoint	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
right_curobo_planner_config	CuroboPlannerConfig | None
right_planner_joint_ranges	dict[str, tuple]
server_timeout	float | None
server_urls	list[str]
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
velocity_constraints	dict[str, float]
verbose	bool

attach_obj class-attribute instance-attribute

attach_obj: bool = False

batch_size class-attribute instance-attribute

batch_size: int = 4

debug_poses class-attribute instance-attribute

debug_poses: bool = False

enable_collision_avoidance class-attribute instance-attribute

enable_collision_avoidance: bool = True

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 8.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.01

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 0.5

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_closed_pos class-attribute instance-attribute

gripper_closed_pos: float = 0.0

gripper_closed_tolerance class-attribute instance-attribute

gripper_closed_tolerance: float = 0.005

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

left_curobo_planner_config class-attribute instance-attribute

left_curobo_planner_config: CuroboPlannerConfig | None = None

left_planner_joint_ranges class-attribute instance-attribute

left_planner_joint_ranges: dict[str, tuple] = {'base': (0, 3), 'left_arm': (3, 10)}

max_batch_plan_attempts class-attribute instance-attribute

max_batch_plan_attempts: int = 4

max_grasping_timesteps class-attribute instance-attribute

max_grasping_timesteps: int = 5

max_opening_timesteps class-attribute instance-attribute

max_opening_timesteps: int = 5

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_planning_reattempts class-attribute instance-attribute

max_planning_reattempts: int = 5

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

max_settle_steps class-attribute instance-attribute

max_settle_steps: int = 5

max_steps_per_waypoint class-attribute instance-attribute

max_steps_per_waypoint: int = 10

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.5

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.05

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.02

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

right_curobo_planner_config class-attribute instance-attribute

right_curobo_planner_config: CuroboPlannerConfig | None = None

right_planner_joint_ranges class-attribute instance-attribute

right_planner_joint_ranges: dict[str, tuple] = {'base': (0, 3), 'right_arm': (3, 10)}

server_timeout class-attribute instance-attribute

server_timeout: float | None = 300.0

server_urls class-attribute instance-attribute

server_urls: list[str] = ['jupiter-cs-aus-107.reviz.ai2.in:10002']

speed_fast class-attribute instance-attribute

speed_fast: float = 0.2

speed_slow class-attribute instance-attribute

speed_slow: float = 0.08

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

velocity_constraints class-attribute instance-attribute

velocity_constraints: dict[str, float] = {'base': 0.5, 'head': 0.5, 'right_arm': 0.5, 'left_arm': 0.5}

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.object_manipulation.pick_and_place_planner_policy import (
            PickAndPlacePlannerPolicy,
        )

        self.policy_cls = PickAndPlacePlannerPolicy
        self.policy_factory = PickAndPlacePlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

DoorOpeningPolicyConfig

Bases: BasePolicyConfig

Configuration for RBY1 door opening planner policy.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
articulation_deltas	list[float]
enable_collision_avoidance	bool
first_pushing_articulation_deltas	list[float]
force_enable_depth	bool	Whether or not to require all cameras to record depth
gripper_closed_pos	float
gripper_closed_tolerance	float
joint_position_tolerance	float
left_curobo_planner_config	CuroboPlannerConfig | None
left_gripper_close_command	dict
left_gripper_open_command	dict
left_planner_joint_ranges	dict[str, tuple]
max_grasping_timesteps	int
max_planning_failures	int
max_steps_per_waypoint	int
model_config
num_recovery_steps	int
plan_in_robot_frame	bool
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
pre_grasp_distance	float
recovery_motion_backward_distance	float
relevant_collision_objects_radius	float
right_curobo_planner_config	CuroboPlannerConfig | None
right_gripper_close_command	dict
right_gripper_open_command	dict
right_planner_joint_ranges	dict[str, tuple]
velocity_constraints	dict[str, float]
verbose	bool

articulation_deltas class-attribute instance-attribute

articulation_deltas: list[float] = [np.pi / 180.0 * 13.0]

enable_collision_avoidance class-attribute instance-attribute

enable_collision_avoidance: bool = True

first_pushing_articulation_deltas class-attribute instance-attribute

first_pushing_articulation_deltas: list[float] = [np.pi / 180.0 * 30.0]

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

gripper_closed_pos class-attribute instance-attribute

gripper_closed_pos: float = 0.0

gripper_closed_tolerance class-attribute instance-attribute

gripper_closed_tolerance: float = 0.005

joint_position_tolerance class-attribute instance-attribute

joint_position_tolerance: float = 0.0275

left_curobo_planner_config class-attribute instance-attribute

left_curobo_planner_config: CuroboPlannerConfig | None = None

left_gripper_close_command class-attribute instance-attribute

left_gripper_close_command: dict = {'left_gripper': 100.0}

left_gripper_open_command class-attribute instance-attribute

left_gripper_open_command: dict = {'left_gripper': -100.0}

left_planner_joint_ranges class-attribute instance-attribute

left_planner_joint_ranges: dict[str, tuple] = {'base': (0, 3), 'left_arm': (3, 10)}

max_grasping_timesteps class-attribute instance-attribute

max_grasping_timesteps: int = 5

max_planning_failures class-attribute instance-attribute

max_planning_failures: int = 15

max_steps_per_waypoint class-attribute instance-attribute

max_steps_per_waypoint: int = 10

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_recovery_steps class-attribute instance-attribute

num_recovery_steps: int = 8

plan_in_robot_frame class-attribute instance-attribute

plan_in_robot_frame: bool = True

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

pre_grasp_distance class-attribute instance-attribute

pre_grasp_distance: float = -0.18

recovery_motion_backward_distance class-attribute instance-attribute

recovery_motion_backward_distance: float = 0.02

relevant_collision_objects_radius class-attribute instance-attribute

relevant_collision_objects_radius: float = 3.0

right_curobo_planner_config class-attribute instance-attribute

right_curobo_planner_config: CuroboPlannerConfig | None = None

right_gripper_close_command class-attribute instance-attribute

right_gripper_close_command: dict = {'right_gripper': 100.0}

right_gripper_open_command class-attribute instance-attribute

right_gripper_open_command: dict = {'right_gripper': -100.0}

right_planner_joint_ranges class-attribute instance-attribute

right_planner_joint_ranges: dict[str, tuple] = {'base': (0, 3), 'right_arm': (3, 10)}

velocity_constraints class-attribute instance-attribute

velocity_constraints: dict[str, float] = {'base': 0.5, 'head': 0.5, 'right_arm': 0.5, 'left_arm': 0.5}

verbose class-attribute instance-attribute

verbose: bool = False

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

DummyPolicyConfig

Bases: BasePolicyConfig

Policy config that uses DummyPolicy for testing.

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
force_enable_depth	bool	Whether or not to require all cameras to record depth
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'dummy'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.dummy_policy import DummyPolicy

        self.policy_cls = DummyPolicy
        self.policy_factory = make_lenient(DummyPolicy)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FetchManBasePlannerPolicyConfig

Bases: NavToObjPlannerPolicyConfig

Configuration for FetchManBasePlannerPolicy -- a port of g1_molmo's navigation policy (molmospaces/agents/policy.py in the g1_molmo reference repo). Unlike AStarPlannerPolicy, which pre-bakes an explicit rotate-then-drive waypoint schedule at plan time, this recomputes a [vx, vy, yaw_rate] base velocity command from the robot's live pose every step (see FetchManBasePlannerPolicy._update_nav_command).

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
brake_dist	float
downscale	int
drive_max_turn	float
face_tol	float
face_turn	float
face_wp_tol	float
final_reach	float
force_enable_depth	bool	Whether or not to require all cameras to record depth
max_turn	float
min_speed	float
model_config
num_recovery_steps	int
plan_max_retries	int
planner_config	AStarPlannerConfig
policy_cls	type
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
recovery_motion_backward_distance	float
simplify_clearance	int
speed	float
standoff_map_extra_inflation	float
standoff_radius_range	tuple[float, float] | None
stop_pad	float
turn_kp	float
verbose	bool
wall_exp	float
wall_gain	float
wall_radius	int
waypoint_reach	float

brake_dist class-attribute instance-attribute

brake_dist: float = 0.7

downscale class-attribute instance-attribute

downscale: int = 4

drive_max_turn class-attribute instance-attribute

drive_max_turn: float = 0.3

face_tol class-attribute instance-attribute

face_tol: float = 0.35

face_turn class-attribute instance-attribute

face_turn: float = 1.2

face_wp_tol class-attribute instance-attribute

face_wp_tol: float = 0.524

final_reach class-attribute instance-attribute

final_reach: float = 0.3

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

max_turn class-attribute instance-attribute

max_turn: float = 1.0

min_speed class-attribute instance-attribute

min_speed: float = 0.15

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_recovery_steps class-attribute instance-attribute

num_recovery_steps: int = 8

plan_max_retries class-attribute instance-attribute

plan_max_retries: int = 3

planner_config class-attribute instance-attribute

planner_config: AStarPlannerConfig = AStarPlannerConfig()

policy_cls class-attribute instance-attribute

policy_cls: type = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

recovery_motion_backward_distance class-attribute instance-attribute

recovery_motion_backward_distance: float = 0.02

simplify_clearance class-attribute instance-attribute

simplify_clearance: int = 6

speed class-attribute instance-attribute

speed: float = 0.4

standoff_map_extra_inflation class-attribute instance-attribute

standoff_map_extra_inflation: float = 0.125

standoff_radius_range class-attribute instance-attribute

standoff_radius_range: tuple[float, float] | None = None

stop_pad class-attribute instance-attribute

stop_pad: float = 0.04

turn_kp class-attribute instance-attribute

turn_kp: float = 2.0

verbose class-attribute instance-attribute

verbose: bool = True

wall_exp class-attribute instance-attribute

wall_exp: float = 2.0

wall_gain class-attribute instance-attribute

wall_gain: float = 6.0

wall_radius class-attribute instance-attribute

wall_radius: int = 10

waypoint_reach class-attribute instance-attribute

waypoint_reach: float = 0.1

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.navigation.fetchman_base_planner_policy import (
            FetchManBasePlannerPolicy,
        )

        self.policy_cls = FetchManBasePlannerPolicy
        self.policy_factory = FetchManBasePlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

NavToObjPlannerPolicyConfig

Bases: BasePolicyConfig

Base configuration for navigation to object planner policies.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
force_enable_depth	bool	Whether or not to require all cameras to record depth
model_config
num_recovery_steps	int
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
recovery_motion_backward_distance	float
verbose	bool

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_recovery_steps class-attribute instance-attribute

num_recovery_steps: int = 8

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

recovery_motion_backward_distance class-attribute instance-attribute

recovery_motion_backward_distance: float = 0.02

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

ObjectManipulationPlannerPolicyConfig

Bases: BasePolicyConfig

Configuration for Franka pick planner policy.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
debug_poses	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_empty_threshold	float
gripper_open_duration	float
max_placement_retries	int
max_retries	int
max_sequential_ik_failures	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
verbose	bool

debug_poses class-attribute instance-attribute

debug_poses: bool = False

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 8.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.01

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 2.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.1

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.05

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.04

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

speed_fast class-attribute instance-attribute

speed_fast: float = 0.2

speed_slow class-attribute instance-attribute

speed_slow: float = 0.08

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

OpenClosePlannerPolicyConfig

Bases: ObjectManipulationPlannerPolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
debug_poses	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_horizontal_cost_weight	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_empty_threshold	float
gripper_open_duration	float
max_placement_retries	int
max_retries	int
max_sequential_ik_failures	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
verbose	bool

debug_poses class-attribute instance-attribute

debug_poses: bool = False

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 0.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_horizontal_cost_weight class-attribute instance-attribute

grasp_horizontal_cost_weight: float = 10.0

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = ['droid']

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.05

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 0.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.2

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.05

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.04

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

speed_fast class-attribute instance-attribute

speed_fast: float = 0.08

speed_slow class-attribute instance-attribute

speed_slow: float = 0.04

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.object_manipulation.open_close_planner_policy import (
            OpenClosePlannerPolicy,
        )

        self.policy_cls = OpenClosePlannerPolicy
        self.policy_factory = OpenClosePlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceColorPlannerPolicyConfig

Bases: PickAndPlacePlannerPolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
debug_poses	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_empty_threshold	float
gripper_open_duration	float
max_placement_retries	int
max_retries	int
max_sequential_ik_failures	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
verbose	bool

debug_poses class-attribute instance-attribute

debug_poses: bool = False

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 8.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.01

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 2.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.5

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.05

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.04

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

speed_fast class-attribute instance-attribute

speed_fast: float = 0.2

speed_slow class-attribute instance-attribute

speed_slow: float = 0.08

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, _context: Any) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    from molmo_spaces.policy.solvers.object_manipulation.pick_and_place_color_planner_policy import (
        PickAndPlaceColorPlannerPolicy,
    )

    self.policy_cls = PickAndPlaceColorPlannerPolicy
    self.policy_factory = PickAndPlaceColorPlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceNextToPlannerPolicyConfig

Bases: PickAndPlacePlannerPolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
debug_poses	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_empty_threshold	float
gripper_open_duration	float
max_placement_retries	int
max_retries	int
max_sequential_ik_failures	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
verbose	bool

debug_poses class-attribute instance-attribute

debug_poses: bool = False

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 8.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.01

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 2.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.5

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.05

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.04

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

speed_fast class-attribute instance-attribute

speed_fast: float = 0.2

speed_slow class-attribute instance-attribute

speed_slow: float = 0.08

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, _context: Any) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    from molmo_spaces.policy.solvers.object_manipulation.pick_and_place_next_to_planner_policy import (
        PickAndPlaceNextToPlannerPolicy,
    )

    self.policy_cls = PickAndPlaceNextToPlannerPolicy
    self.policy_factory = PickAndPlaceNextToPlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlacePlannerPolicyConfig

Bases: ObjectManipulationPlannerPolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
debug_poses	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_empty_threshold	float
gripper_open_duration	float
max_placement_retries	int
max_retries	int
max_sequential_ik_failures	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
verbose	bool

debug_poses class-attribute instance-attribute

debug_poses: bool = False

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 8.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.01

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 2.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.5

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.05

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.04

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

speed_fast class-attribute instance-attribute

speed_fast: float = 0.2

speed_slow class-attribute instance-attribute

speed_slow: float = 0.08

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.object_manipulation.pick_and_place_planner_policy import (
            PickAndPlacePlannerPolicy,
        )

        self.policy_cls = PickAndPlacePlannerPolicy
        self.policy_factory = PickAndPlacePlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickPlannerPolicyConfig

Bases: ObjectManipulationPlannerPolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
debug_poses	bool
end_z_offset	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasp_base_pos	list[float]
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_empty_threshold	float
gripper_open_duration	float
max_placement_retries	int
max_retries	int
max_sequential_ik_failures	int
model_config
move_settle_time	float
phase_timeout	float
place_z_offset	float
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
speed_fast	float
speed_slow	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
verbose	bool

debug_poses class-attribute instance-attribute

debug_poses: bool = False

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 8.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW,)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.01

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 2.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = 0.002

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 0.1

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.08

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.04

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

speed_fast class-attribute instance-attribute

speed_fast: float = 0.2

speed_slow class-attribute instance-attribute

speed_slow: float = 0.08

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 0.1

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(30.0)

verbose class-attribute instance-attribute

verbose: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.object_manipulation.pick_planner_policy import (
            PickPlannerPolicy,
        )

        self.policy_cls = PickPlannerPolicy
        self.policy_factory = PickPlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

policy_configs_baselines

Classes:

Name	Description
BimanualYamPiPolicyConfig	Configuration for BimanualYamPiPolicy using LeRobot gRPC server.
CAPPolicyConfig
DreamZeroPolicyConfig
PiPolicyConfig
TeleopPolicyConfig

BimanualYamPiPolicyConfig

Bases: BasePolicyConfig

Configuration for BimanualYamPiPolicy using LeRobot gRPC server.

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
buffer_length	int
camera_mapping	dict
checkpoint_path	str
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasping_type	str
model_config
name	str
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
remote_config	dict

buffer_length class-attribute instance-attribute

buffer_length: int = 50

camera_mapping class-attribute instance-attribute

camera_mapping: dict = dict(left_wrist_camera='observation.images.left', right_wrist_camera='observation.images.right', exo_camera='observation.images.top')

checkpoint_path class-attribute instance-attribute

checkpoint_path: str = 'Jiafei1224/ppack200k'

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasping_type class-attribute instance-attribute

grasping_type: str = 'binary'

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str = 'bimanual_yam_pi'

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'learned'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

remote_config class-attribute instance-attribute

remote_config: dict = dict(host='triton-cs-aus-454.reviz.ai2.in', port=8060, policy_type='pi05', device='cuda')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs_baselines.py

def model_post_init(self, _context: Any) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(_context)
    if self.policy_cls is None:
        from molmo_spaces.policy.learned_policy.bimanual_yam_pi_policy import (
            BimanualYamPiPolicy,
        )

        self.policy_cls = BimanualYamPiPolicy
        self.policy_factory = make_lenient(BimanualYamPiPolicy)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

CAPPolicyConfig

Bases: BasePolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
exo_vlm	bool
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasping_threshold	float
grasping_type	str
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
remote_config	dict
use_vlm	bool

exo_vlm class-attribute instance-attribute

exo_vlm: bool = True

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasping_threshold class-attribute instance-attribute

grasping_threshold: float = 0.7

grasping_type class-attribute instance-attribute

grasping_type: str = 'binary'

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'learned'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

remote_config class-attribute instance-attribute

remote_config: dict = dict(host='localhost', port=8765)

use_vlm class-attribute instance-attribute

use_vlm: bool = False

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs_baselines.py

def model_post_init(self, __context) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.learned_policy.cap_policy import CAP_Policy

        self.policy_cls = CAP_Policy
        self.policy_factory = make_lenient(CAP_Policy)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

DreamZeroPolicyConfig

Bases: BasePolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
checkpoint_path	str
chunk_size	int
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasping_threshold	float
grasping_type	str
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
prompt_level	int
prompt_object_word_num	int
prompt_templates	list[str] | None
remote_config	dict
use_prompt_sampler	bool

checkpoint_path class-attribute instance-attribute

checkpoint_path: str = 'checkpoints/dreamzero'

chunk_size class-attribute instance-attribute

chunk_size: int = 24

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasping_threshold class-attribute instance-attribute

grasping_threshold: float = 0.5

grasping_type class-attribute instance-attribute

grasping_type: str = 'binary'

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'learned'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

prompt_level class-attribute instance-attribute

prompt_level: int = 2

prompt_object_word_num class-attribute instance-attribute

prompt_object_word_num: int = 1

prompt_templates class-attribute instance-attribute

prompt_templates: list[str] | None = None

remote_config class-attribute instance-attribute

remote_config: dict = dict(host='localhost', port=0)

use_prompt_sampler class-attribute instance-attribute

use_prompt_sampler: bool = False

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs_baselines.py

def model_post_init(self, _context: Any) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(_context)
    if self.policy_cls is None:
        from molmo_spaces.policy.learned_policy.dreamzero_policy import DreamZero_Policy

        self.policy_cls = DreamZero_Policy
        self.policy_factory = make_lenient(DreamZero_Policy)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PiPolicyConfig

Bases: BasePolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
checkpoint_path	str
chunk_size	int
force_enable_depth	bool	Whether or not to require all cameras to record depth
grasping_threshold	float
grasping_type	str
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
prompt_level	int
prompt_object_word_num	int
prompt_templates	list[str] | None
remote_config	dict | None
use_prompt_sampler	bool

checkpoint_path class-attribute instance-attribute

checkpoint_path: str = 'checkpoints/pi'

chunk_size class-attribute instance-attribute

chunk_size: int = 8

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

grasping_threshold class-attribute instance-attribute

grasping_threshold: float = 0.5

grasping_type class-attribute instance-attribute

grasping_type: str = 'binary'

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'learned'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

prompt_level class-attribute instance-attribute

prompt_level: int = 2

prompt_object_word_num class-attribute instance-attribute

prompt_object_word_num: int = 1

prompt_templates class-attribute instance-attribute

prompt_templates: list[str] | None = None

remote_config class-attribute instance-attribute

remote_config: dict | None = dict(host='localhost', port=8080)

use_prompt_sampler class-attribute instance-attribute

use_prompt_sampler: bool = False

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs_baselines.py

def model_post_init(self, _context: Any) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(_context)
    if self.policy_cls is None:
        from molmo_spaces.policy.learned_policy.pi_policy import PI_Policy

        self.policy_cls = PI_Policy
        self.policy_factory = make_lenient(PI_Policy)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

TeleopPolicyConfig

Bases: BasePolicyConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
device	str
force_enable_depth	bool	Whether or not to require all cameras to record depth
model_config
policy_cls	type[BasePolicy] | None
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
pos_sensitivity	float
product_id	int
rot_sensitivity	float
rot_step	float
step_size	float

device class-attribute instance-attribute

device: str = 'keyboard'

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

policy_cls class-attribute instance-attribute

policy_cls: type[BasePolicy] | None = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'teleop'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

pos_sensitivity class-attribute instance-attribute

pos_sensitivity: float = 0.005

product_id class-attribute instance-attribute

product_id: int = 50741

rot_sensitivity class-attribute instance-attribute

rot_sensitivity: float = 0.02

rot_step class-attribute instance-attribute

rot_step: float = 0.02

step_size class-attribute instance-attribute

step_size: float = 0.005

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs_baselines.py

def model_post_init(self, _context: Any) -> None:
    """Set policy_cls after initialization to avoid circular imports."""
    super().model_post_init(_context)
    if self.policy_cls is None:
        if self.device == "keyboard":
            from molmo_spaces.policy.learned_policy.keyboard_policy import Keyboard_Policy

            self.policy_cls = Keyboard_Policy
            self.policy_factory = make_lenient(Keyboard_Policy)
        elif self.device == "spacemouse":
            from molmo_spaces.policy.learned_policy.spacemouse_policy import SpaceMouse_Policy

            self.policy_cls = SpaceMouse_Policy
            self.policy_factory = make_lenient(SpaceMouse_Policy)
        elif self.device == "phone":
            from molmo_spaces.policy.learned_policy.phone_policy import Phone_Policy

            self.policy_cls = Phone_Policy
            self.policy_factory = make_lenient(Phone_Policy)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

policy_configs_fetchman_pick

Configuration for :class:G1PickPlannerPolicy (the "Fetchman" G1 pick).

Classes:

Name	Description
FetchmanPickPlannerPolicyConfig	G1 pick with mink whole-body IK (waist + pelvis height assist the reach)

FetchmanPickPlannerPolicyConfig

Bases: PickPlannerPolicyConfig

G1 pick with mink whole-body IK (waist + pelvis height assist the reach) and a walk phase. Requires G1Config's WBC-walking mode. Values are g1_molmo's unless a comment says why they differ.

Methods:

Name	Description
from_dict
load_from_json
model_post_init	Set policy_cls after initialization to avoid circular imports.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
brake_dist	float
debug_poses	bool
direct_arrival_max_dist	float
direct_walk	bool
downscale	int
drive_max_turn	float
end_z_offset	float
face_tol	float
face_turn	float
face_wp_tol	float
filter_colliding_grasps	bool
filter_feasible_grasps	bool
final_reach	float
force_enable_depth	bool	Whether or not to require all cameras to record depth
force_standoff_walk	bool
goal_sampling_attempts	int
goal_standoff_radius_range	tuple[float, float]
grasp_base_pos	list[float]
grasp_candidates_to_try	int
grasp_collision_batch_size	int
grasp_collision_max_grasps	int
grasp_com_dist_cost_weight	float
grasp_feasibility_batch_size	int
grasp_feasibility_max_grasps	int
grasp_height	float
grasp_horizontal_cost_weight	float
grasp_length	float
grasp_libraries	list[str] | None
grasp_pos_cost_weight	float
grasp_probe_shapes	tuple[str, ...]
grasp_rot_cost_weight	float
grasp_vertical_cost_weight	float
grasp_width	float
grasp_xy_noise	float
grasp_yaw_noise	float
grasp_z_offset	float
gripper_close_duration	float
gripper_empty_threshold	float
gripper_open_duration	float
height_waist_smoothing_alpha	float
max_placement_retries	int
max_retries	int
max_sequential_ik_failures	int
max_turn	float
min_speed	float
model_config
move_settle_time	float
nav_map_extra_inflation	float
phase_timeout	float
place_z_offset	float
planner_config	AStarPlannerConfig
policy_cls	type
policy_factory	PolicyFactory | None	Factory function to create the policy instance from a config and task, can be same as policy_cls
policy_type	str	Type of the policy, e.g., "planner", "teleop", "learned", etc.
postgrasp_height_noise	float
postgrasp_z_offset	float
pregrasp_height_noise	float
pregrasp_z_offset	float
randomize_grasp	bool
simplify_clearance	int
speed	float
speed_fast	float
speed_slow	float
stop_pad	float
tcp_pos_err_threshold	float
tcp_rot_err_threshold	float
turn_kp	float
verbose	bool
walk_goal_distance_threshold	float
walk_timeout_s	float
walk_timeout_slack	float
wall_exp	float
wall_gain	float
wall_radius	int
waypoint_reach	float

brake_dist class-attribute instance-attribute

brake_dist: float = 0.7

debug_poses class-attribute instance-attribute

debug_poses: bool = False

direct_arrival_max_dist class-attribute instance-attribute

direct_arrival_max_dist: float = 1.2

direct_walk class-attribute instance-attribute

direct_walk: bool = False

downscale class-attribute instance-attribute

downscale: int = 4

drive_max_turn class-attribute instance-attribute

drive_max_turn: float = 0.3

end_z_offset class-attribute instance-attribute

end_z_offset: float = 0.05

face_tol class-attribute instance-attribute

face_tol: float = 0.35

face_turn class-attribute instance-attribute

face_turn: float = 1.2

face_wp_tol class-attribute instance-attribute

face_wp_tol: float = 0.524

filter_colliding_grasps class-attribute instance-attribute

filter_colliding_grasps: bool = True

filter_feasible_grasps class-attribute instance-attribute

filter_feasible_grasps: bool = True

final_reach class-attribute instance-attribute

final_reach: float = 0.3

force_enable_depth class-attribute instance-attribute

force_enable_depth: bool = False

Whether or not to require all cameras to record depth

force_standoff_walk class-attribute instance-attribute

force_standoff_walk: bool = False

goal_sampling_attempts class-attribute instance-attribute

goal_sampling_attempts: int = 25

goal_standoff_radius_range class-attribute instance-attribute

goal_standoff_radius_range: tuple[float, float] = (0.45, 0.58)

grasp_base_pos class-attribute instance-attribute

grasp_base_pos: list[float] = [0.0, 0.0, -0.04]

grasp_candidates_to_try class-attribute instance-attribute

grasp_candidates_to_try: int = 5

grasp_collision_batch_size class-attribute instance-attribute

grasp_collision_batch_size: int = 1

grasp_collision_max_grasps class-attribute instance-attribute

grasp_collision_max_grasps: int = 512

grasp_com_dist_cost_weight class-attribute instance-attribute

grasp_com_dist_cost_weight: float = 8.0

grasp_feasibility_batch_size class-attribute instance-attribute

grasp_feasibility_batch_size: int = 256

grasp_feasibility_max_grasps class-attribute instance-attribute

grasp_feasibility_max_grasps: int = 256

grasp_height class-attribute instance-attribute

grasp_height: float = 0.01

grasp_horizontal_cost_weight class-attribute instance-attribute

grasp_horizontal_cost_weight: float = 2.0

grasp_length class-attribute instance-attribute

grasp_length: float = 0.05

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

grasp_pos_cost_weight class-attribute instance-attribute

grasp_pos_cost_weight: float = 1.0

grasp_probe_shapes class-attribute instance-attribute

grasp_probe_shapes: tuple[str, ...] = (spec_ops.PROBE_SHAPE_JAW, spec_ops.PROBE_SHAPE_GRIPPER_XML)

grasp_rot_cost_weight class-attribute instance-attribute

grasp_rot_cost_weight: float = 0.01

grasp_vertical_cost_weight class-attribute instance-attribute

grasp_vertical_cost_weight: float = 0.0

grasp_width class-attribute instance-attribute

grasp_width: float = 0.08

grasp_xy_noise class-attribute instance-attribute

grasp_xy_noise: float = 0.02

grasp_yaw_noise class-attribute instance-attribute

grasp_yaw_noise: float = 0.5

grasp_z_offset class-attribute instance-attribute

grasp_z_offset: float = 0.03

gripper_close_duration class-attribute instance-attribute

gripper_close_duration: float = 0.5

gripper_empty_threshold class-attribute instance-attribute

gripper_empty_threshold: float = float('-inf')

gripper_open_duration class-attribute instance-attribute

gripper_open_duration: float = 0.25

height_waist_smoothing_alpha class-attribute instance-attribute

height_waist_smoothing_alpha: float = 0.1

max_placement_retries class-attribute instance-attribute

max_placement_retries: int = 0

max_retries class-attribute instance-attribute

max_retries: int = 3

max_sequential_ik_failures class-attribute instance-attribute

max_sequential_ik_failures: int = 8

max_turn class-attribute instance-attribute

max_turn: float = 1.0

min_speed class-attribute instance-attribute

min_speed: float = 0.15

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

move_settle_time class-attribute instance-attribute

move_settle_time: float = 1.0

nav_map_extra_inflation class-attribute instance-attribute

nav_map_extra_inflation: float = 0.125

phase_timeout class-attribute instance-attribute

phase_timeout: float = 10.0

place_z_offset class-attribute instance-attribute

place_z_offset: float = 0.07

planner_config class-attribute instance-attribute

planner_config: AStarPlannerConfig = AStarPlannerConfig()

policy_cls class-attribute instance-attribute

policy_cls: type = None

policy_factory class-attribute instance-attribute

policy_factory: PolicyFactory | None = None

Factory function to create the policy instance from a config and task, can be same as policy_cls

policy_type class-attribute instance-attribute

policy_type: str = 'planner'

Type of the policy, e.g., "planner", "teleop", "learned", etc.

postgrasp_height_noise class-attribute instance-attribute

postgrasp_height_noise: float = 0.02

postgrasp_z_offset class-attribute instance-attribute

postgrasp_z_offset: float = 0.15

pregrasp_height_noise class-attribute instance-attribute

pregrasp_height_noise: float = 0.03

pregrasp_z_offset class-attribute instance-attribute

pregrasp_z_offset: float = 0.04

randomize_grasp class-attribute instance-attribute

randomize_grasp: bool = False

simplify_clearance class-attribute instance-attribute

simplify_clearance: int = 6

speed class-attribute instance-attribute

speed: float = 0.4

speed_fast class-attribute instance-attribute

speed_fast: float = 0.08

speed_slow class-attribute instance-attribute

speed_slow: float = 0.08

stop_pad class-attribute instance-attribute

stop_pad: float = 0.04

tcp_pos_err_threshold class-attribute instance-attribute

tcp_pos_err_threshold: float = 1.0

tcp_rot_err_threshold class-attribute instance-attribute

tcp_rot_err_threshold: float = np.radians(60.0)

turn_kp class-attribute instance-attribute

turn_kp: float = 2.0

verbose class-attribute instance-attribute

verbose: bool = True

walk_goal_distance_threshold class-attribute instance-attribute

walk_goal_distance_threshold: float = 0.5

walk_timeout_s class-attribute instance-attribute

walk_timeout_s: float = 20.0

walk_timeout_slack class-attribute instance-attribute

walk_timeout_slack: float = 2.0

wall_exp class-attribute instance-attribute

wall_exp: float = 2.0

wall_gain class-attribute instance-attribute

wall_gain: float = 6.0

wall_radius class-attribute instance-attribute

wall_radius: int = 10

waypoint_reach class-attribute instance-attribute

waypoint_reach: float = 0.1

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Set policy_cls after initialization to avoid circular imports.

Source code in molmo_spaces/configs/policy_configs_fetchman_pick.py

def model_post_init(self, __context) -> None:
    # Skip PickPlannerPolicyConfig's, which would set policy_cls to PickPlannerPolicy.
    super(PickPlannerPolicyConfig, self).model_post_init(__context)
    if self.policy_cls is None:
        from molmo_spaces.policy.solvers.object_manipulation.pick_planner_policy_g1 import (
            G1PickPlannerPolicy,
        )

        self.policy_cls = G1PickPlannerPolicy
        self.policy_factory = G1PickPlannerPolicy

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

robot_configs

Robot configuration classes for MolmoSpaces experiments.

This module contains: - ActionNoiseConfig: TCP-bounded noise configuration for arm actions - BaseRobotConfig: Base configuration for all robots - Robot-specific configs: FrankaRobotConfig, RBY1Config, FloatingRUMRobotConfig

Classes:

Name	Description
ActionNoiseConfig	Configuration for action noise injection.
BaseRobotConfig	Base configuration for robot setup.
BimanualYamRobotConfig	Configuration for bimanual YAM robot (two 6-DOF arms with parallel grippers).
FloatingRUMRobotConfig
FloatingRobotiq2f85RobotConfig
FrankaCAPRobotConfig	Configuration for Franka FR3 robot.
FrankaRobotConfig	Configuration for Franka FR3 robot.
G1Config	Configuration for the Unitree G1 humanoid robot.
I2rtYamRobotConfig	Configuration for i2rt YAM 6-DOF robot.
MobileFrankaRobotConfig
RBY1Config	Configuration for RBY1 robot.
RBY1MConfig	Configuration for RBY1M i.e. mecanum wheel robot.
RBY1MOpenCloseConfig	RBY1M config for open/close tasks.

ActionNoiseConfig

Bases: Config

Configuration for action noise injection.

This noise model supports: - Arm noise: TCP-bounded noise that maps through Jacobian to joint space - Base noise: Planar noise applied directly to (x, y, theta) commands

Noise is proportional to the commanded action magnitude

noise_std = action_scale_factor * ||delta||

When the commanded delta is zero, no noise is applied.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_scale_factor	float
base_action_scale_factor	float
enabled	bool
max_base_position_noise	float
max_base_rotation_noise	float
max_tcp_position_noise	float
max_tcp_rotation_noise	float
model_config
rotation_noise_scale	float

action_scale_factor class-attribute instance-attribute

action_scale_factor: float = 0.1

base_action_scale_factor class-attribute instance-attribute

base_action_scale_factor: float = 0.1

enabled class-attribute instance-attribute

enabled: bool = True

max_base_position_noise class-attribute instance-attribute

max_base_position_noise: float = 0.02

max_base_rotation_noise class-attribute instance-attribute

max_base_rotation_noise: float = 0.05

max_tcp_position_noise class-attribute instance-attribute

max_tcp_position_noise: float = 0.02

max_tcp_rotation_noise class-attribute instance-attribute

max_tcp_rotation_noise: float = 0.1

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

rotation_noise_scale class-attribute instance-attribute

rotation_noise_scale: float = 0.1

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

BaseRobotConfig

Bases: Config

Base configuration for robot setup.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

command_mode instance-attribute

command_mode: dict[str, str | None]

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = False

init_qpos instance-attribute

init_qpos: dict[str, list[float]]

init_qpos_noise_range instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name instance-attribute

name: str | None

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls instance-attribute

robot_cls: type[Robot] | None

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None

robot_namespace instance-attribute

robot_namespace: str

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory instance-attribute

robot_view_factory: RobotViewFactory | None

robot_xml_path instance-attribute

robot_xml_path: Path

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    """Ensure action_noise_config is always initialized, even when loading from old configs."""
    if self.action_noise_config is None:
        object.__setattr__(self, "action_noise_config", ActionNoiseConfig())

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

BimanualYamRobotConfig

Bases: BaseRobotConfig

Configuration for bimanual YAM robot (two 6-DOF arms with parallel grippers).

The bimanual YAM consists of two YAM arms positioned 44cm apart, both facing forward.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
base_size	list[float] | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

base_size class-attribute instance-attribute

base_size: list[float] | None = [0.3, 0.8, 0.7]

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'arm': 'joint_position', 'gripper': 'joint_position'}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'left_arm': [0.0624, 0.0109, 0.1707, -0.5938, 0.411, 0.3401], 'right_arm': [0.0006, 0.0147, 0.1669, -0.6407, 0.0746, 0.1516], 'left_gripper': [0.03914, 0.0], 'right_gripper': [0.04068, 0.0]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'i2rt_yam'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = BimanualYamRobot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = BimanualYamRobot

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = BimanualYamRobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('bimanual_yam.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context: Any):
    super().model_post_init(_context)
    if "gripper" in self.command_mode:
        assert self.command_mode["gripper"] == "joint_position"
    if "arm" in self.command_mode:
        assert self.command_mode["arm"] in ["joint_position", "joint_rel_position"]

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FloatingRUMRobotConfig

Bases: BaseRobotConfig

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
command_mode	dict	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
ctrl_dt_ms	float
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

command_mode class-attribute instance-attribute

command_mode: dict = {}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 50.0

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = False

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list] = {'gripper': [0.0, 0.0]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'floating_rum'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = FloatingRUMRobot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = FloatingRUMRobot

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = FloatingRUMRobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('model.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    """Ensure action_noise_config is always initialized, even when loading from old configs."""
    if self.action_noise_config is None:
        object.__setattr__(self, "action_noise_config", ActionNoiseConfig())

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FloatingRobotiq2f85RobotConfig

Bases: BaseRobotConfig

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
action_spec	dict[str, int]
command_mode	dict	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
ctrl_dt_ms	float
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, BaseRobotConfig], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

action_spec class-attribute instance-attribute

action_spec: dict[str, int] = {'base': 7, 'gripper': 2}

command_mode class-attribute instance-attribute

command_mode: dict = {}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

ctrl_dt_ms class-attribute instance-attribute

ctrl_dt_ms: float = 50.0

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = False

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list] = {'gripper': [0.00296, 0.00296]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'floating_robotiq'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = FloatingRobotiqRobot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, BaseRobotConfig], Robot] | None = FloatingRobotiqRobot

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = FloatingRobotiq2f85RobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('model.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    """Ensure action_noise_config is always initialized, even when loading from old configs."""
    if self.action_noise_config is None:
        object.__setattr__(self, "action_noise_config", ActionNoiseConfig())

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaCAPRobotConfig

Bases: BaseRobotConfig

Configuration for Franka FR3 robot.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
base_size	list[float] | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

base_size class-attribute instance-attribute

base_size: list[float] | None = [0.5, 0.5, 0.58]

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'arm': 'joint_position', 'gripper': 'joint_position'}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'arm': [0, -1.5, 0.116, -2.45, 0, 0.842, 0.965], 'gripper': [0.00296, 0.00296]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {'arm': [0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175]}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'franka_cap'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = FrankaRobot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = FrankaRobot

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = FrankaCAPRobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('model.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context: Any):
    super().model_post_init(_context)
    if "gripper" in self.command_mode:
        assert self.command_mode["gripper"] == "joint_position"
    if "arm" in self.command_mode:
        assert self.command_mode["arm"] in ["joint_position", "joint_rel_position"]

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

FrankaRobotConfig

Bases: BaseRobotConfig

Configuration for Franka FR3 robot.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
base_size	list[float] | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
perturb_texture_probability	float
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

base_size class-attribute instance-attribute

base_size: list[float] | None = [0.5, 0.5, 0.58]

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'arm': 'joint_position', 'gripper': 'joint_position'}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'arm': [0, -0.7853, 0, -2.35619, 0, 1.57079, 0.0], 'gripper': [0.00296, 0.00296]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {'arm': [0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175]}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'franka_droid'

perturb_texture_probability class-attribute instance-attribute

perturb_texture_probability: float = 0.7

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = FrankaRobot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = FrankaRobot

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = FrankaDroidRobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('model.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context: Any):
    super().model_post_init(_context)
    if "gripper" in self.command_mode:
        assert self.command_mode["gripper"] == "joint_position"
    if "arm" in self.command_mode:
        assert self.command_mode["arm"] in ["joint_position", "joint_rel_position"]

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

G1Config

Bases: BaseRobotConfig

Configuration for the Unitree G1 humanoid robot.

One base control mode: - Whole-body walking: the combined legs_waist move group is driven by the WBC (a PD-torque law plus an ONNX walking policy -- see molmo_spaces.controllers.g1_wbc), commanded via set_target([vx, vy, yaw_rate, height, waist_yaw, waist_roll, waist_pitch]). The base has no actuators (free-floating pelvis integrated directly by MuJoCo's physics).

Both arms and the right gripper always stay on plain JointPosControllers, relying on the MJCF's own tuned PD actuator gains.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
gripper_friction	tuple[float, float, float] | None
init_qpos	dict[str, ndarray]
init_qpos_noise_range	dict[str, ndarray] | None
model_config
name	str
physics_timestep	float
robot_cls	type[G1Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'legs_waist': 'joint_position', 'arm': 'joint_position', 'gripper': 'joint_position'}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = 0.793

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = False

gripper_friction class-attribute instance-attribute

gripper_friction: tuple[float, float, float] | None = None

init_qpos class-attribute instance-attribute

init_qpos: dict[str, ndarray] = {'legs_waist': np.array([-0.312, 0.0, 0.0, 0.669, -0.363, 0.0, -0.312, 0.0, 0.0, 0.669, -0.363, 0.0, 0.0, 0.0, 0.0]), 'left_arm': np.array([0.212, -0.017, 0.062, 1.216, 0.005, 0.258, 0.006]), 'right_arm': np.array([0.2, -0.2, 0.0, -0.2, 0.0, 0.0, 0.0]), 'right_gripper': np.array([-0.0222])}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, ndarray] | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str = 'g1'

physics_timestep class-attribute instance-attribute

physics_timestep: float = 0.005

robot_cls class-attribute instance-attribute

robot_cls: type[G1Robot] | None = G1Robot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = G1Robot.from_mj_data

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = G1RobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('g1_dex.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    """Ensure action_noise_config is always initialized, even when loading from old configs."""
    if self.action_noise_config is None:
        object.__setattr__(self, "action_noise_config", ActionNoiseConfig())

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

I2rtYamRobotConfig

Bases: BaseRobotConfig

Configuration for i2rt YAM 6-DOF robot.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
base_size	list[float] | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

base_size class-attribute instance-attribute

base_size: list[float] | None = [0.3, 0.3, 0.7]

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'arm': 'joint_position', 'gripper': 'joint_position'}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'arm': [0.0, 1.047, 1.047, 0.1, -0.1, 0.0], 'gripper': [0.0, 0.0]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'i2rt_yam'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = I2rtYamRobot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = I2rtYamRobot

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = I2rtYamRobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('yam.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context: Any)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context: Any):
    super().model_post_init(_context)
    if "gripper" in self.command_mode:
        assert self.command_mode["gripper"] == "joint_position"
    if "arm" in self.command_mode:
        assert self.command_mode["arm"] in ["joint_position", "joint_rel_position"]

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

MobileFrankaRobotConfig

Bases: BaseRobotConfig

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
base_control_params	dict[str, dict[str, float]]
base_size	list[float]
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

base_control_params class-attribute instance-attribute

base_control_params: dict[str, dict[str, float]] = {'base_x_act': {'kp': 25000, 'damping_ratio': 1.0, 'ctrlrange': 25}, 'base_y_act': {'kp': 25000, 'damping_ratio': 1.0, 'ctrlrange': 25}, 'base_theta_act': {'kp': 5000, 'damping_ratio': 1.0}}

base_size class-attribute instance-attribute

base_size: list[float] = [0.5, 0.5, 0.58]

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'base': 'holo_joint_planar_position', 'arm': 'joint_position', 'gripper': 'joint_position'}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'base': [0, 0, 0], 'arm': [0, -0.7853, 0, -2.35619, 0, 1.57079, 0.0], 'gripper': [0.00296, 0.00296]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {'arm': [0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175]}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'franka_droid'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = MobileFrankaRobot

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = MobileFrankaRobot

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = MobileFrankaDroidRobotView

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('model.xml')

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    """Ensure action_noise_config is always initialized, even when loading from old configs."""
    if self.action_noise_config is None:
        object.__setattr__(self, "action_noise_config", ActionNoiseConfig())

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RBY1Config

Bases: BaseRobotConfig

Configuration for RBY1 robot.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path
use_holo_base	bool

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'arm': 'joint_position', 'gripper': 'joint_position', 'base': 'holo_joint_planar_position', 'head': None}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'base': [0.0, 0.0, 0.0], 'head': [0.0, 0.6], 'left_arm': [0.5, 0.0, 0.0, -2.3, 0.0, -0.5, 0.0], 'left_gripper': [-0.05], 'right_arm': [0.5, 0.0, 0.0, -2.3, 0.0, -0.5, 0.0], 'right_gripper': [-0.05], 'torso': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {'base': [0.0, 0.0, 0.0], 'head': [0.2, 0.2], 'left_arm': [0.05, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175], 'left_gripper': [0.01], 'right_arm': [0.05, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175], 'right_gripper': [0.01], 'torso': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'rby1'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = RBY1

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = RBY1

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = None

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('rby1_site_control.xml')

use_holo_base class-attribute instance-attribute

use_holo_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    super().model_post_init(_context)
    self.robot_view_factory = partial(RBY1RobotView, holo_base=self.use_holo_base)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RBY1MConfig

Bases: RBY1Config

Configuration for RBY1M i.e. mecanum wheel robot.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path
use_holo_base	bool

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'arm': 'joint_position', 'gripper': 'joint_position', 'base': 'holo_joint_planar_position', 'head': None}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'base': [0.0, 0.0, 0.0], 'head': [0.0, 0.6], 'left_arm': [0.5, 0.0, 0.0, -2.3, 0.0, -0.5, 0.0], 'left_gripper': [-0.05], 'right_arm': [0.5, 0.0, 0.0, -2.3, 0.0, -0.5, 0.0], 'right_gripper': [-0.05], 'torso': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {'base': [0.0, 0.0, 0.0], 'head': [0.2, 0.2], 'left_arm': [0.05, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175], 'left_gripper': [0.01], 'right_arm': [0.05, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175], 'right_gripper': [0.01], 'torso': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'rby1m'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = RBY1

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = RBY1

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = None

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('rby1_v1.2_site_control.xml')

use_holo_base class-attribute instance-attribute

use_holo_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    super().model_post_init(_context)
    self.robot_view_factory = partial(RBY1RobotView, holo_base=self.use_holo_base)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RBY1MOpenCloseConfig

Bases: RBY1MConfig

RBY1M config for open/close tasks.

Uses single-scalar torso height control (torso_1 = torso_3 = h, torso_2 = -2*h) instead of commanding all 6 torso joints independently.

Methods:

Name	Description
from_dict
get_robot_dir	Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
get_robot_xml_path	Get the full path to the robot XML file.
load_from_json
model_post_init	Ensure action_noise_config is always initialized, even when loading from old configs.
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
K_damping	list[float] | None
K_stiffness	list[float] | None
action_noise_config	ActionNoiseConfig | None
command_mode	dict[str, str | None]	move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'
fixed_base_height	float | None
force_limit	list[float] | None
gravcomp	bool
init_qpos	dict[str, list[float]]
init_qpos_noise_range	dict[str, list[float]] | None
model_config
name	str | None
physics_timestep	float | None
robot_cls	type[Robot] | None
robot_dir	Path | None
robot_factory	Callable[[MjData, Any], Robot] | None
robot_namespace	str	Namespace used to differentiate between one or multiple robots and the environment
robot_view_factory	RobotViewFactory | None
robot_xml_path	Path
use_holo_base	bool

K_damping class-attribute instance-attribute

K_damping: list[float] | None = None

K_stiffness class-attribute instance-attribute

K_stiffness: list[float] | None = None

action_noise_config class-attribute instance-attribute

action_noise_config: ActionNoiseConfig | None = None

command_mode class-attribute instance-attribute

command_mode: dict[str, str | None] = {'arm': 'joint_rel_position', 'gripper': 'joint_position', 'base': 'holo_joint_rel_planar_position', 'head': None, 'torso': 'height'}

move_group to command_mode e.g., 'joint', 'cartesian', 'velocity'

fixed_base_height class-attribute instance-attribute

fixed_base_height: float | None = None

force_limit class-attribute instance-attribute

force_limit: list[float] | None = None

gravcomp class-attribute instance-attribute

gravcomp: bool = True

init_qpos class-attribute instance-attribute

init_qpos: dict[str, list[float]] = {'base': [0.0, 0.0, 0.0], 'head': [0.0, 0.6], 'left_arm': [0.5, 0.0, 0.0, -2.3, 0.0, -0.5, 0.0], 'left_gripper': [-0.05], 'right_arm': [0.5, 0.0, 0.0, -2.3, 0.0, -0.5, 0.0], 'right_gripper': [-0.05], 'torso': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

init_qpos_noise_range class-attribute instance-attribute

init_qpos_noise_range: dict[str, list[float]] | None = {'base': [0.0, 0.0, 0.0], 'head': [0.2, 0.2], 'left_arm': [0.05, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175], 'left_gripper': [0.01], 'right_arm': [0.05, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175], 'right_gripper': [0.01], 'torso': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

name class-attribute instance-attribute

name: str | None = 'rby1m'

physics_timestep class-attribute instance-attribute

physics_timestep: float | None = None

robot_cls class-attribute instance-attribute

robot_cls: type[Robot] | None = RBY1

robot_dir class-attribute instance-attribute

robot_dir: Path | None = None

robot_factory class-attribute instance-attribute

robot_factory: Callable[[MjData, Any], Robot] | None = RBY1

robot_namespace class-attribute instance-attribute

robot_namespace: str = 'robot_0/'

Namespace used to differentiate between one or multiple robots and the environment

robot_view_factory class-attribute instance-attribute

robot_view_factory: RobotViewFactory | None = None

robot_xml_path class-attribute instance-attribute

robot_xml_path: Path = Path('rby1_v1.2_site_control.xml')

use_holo_base class-attribute instance-attribute

use_holo_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

get_robot_dir

get_robot_dir() -> Path

Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_dir(self) -> Path:
    """
    Get the path to the robot directory, which may or may not be a prepackaged MlSpaces robot.
    """
    if self.robot_dir is not None:
        return self.robot_dir
    return get_robot_path(self.name)

get_robot_xml_path

get_robot_xml_path() -> Path

Get the full path to the robot XML file.

Source code in molmo_spaces/configs/robot_configs.py

def get_robot_xml_path(self) -> Path:
    """
    Get the full path to the robot XML file.
    """
    return self.get_robot_dir() / self.robot_xml_path

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(_context)

Ensure action_noise_config is always initialized, even when loading from old configs.

Source code in molmo_spaces/configs/robot_configs.py

def model_post_init(self, _context):
    super().model_post_init(_context)
    self.robot_view_factory = partial(RBY1RobotView, holo_base=self.use_holo_base)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

task_configs

Task configuration classes for MolmoSpaces experiments.

Classes:

Name	Description
BaseMujocoTaskConfig	Base configuration for MuJoCo tasks.
DoorOpeningTaskConfig	Configuration for RBY1 door opening task.
NavToObjTaskConfig	Configuration for RBY1 navigation to object task.
OpeningTaskConfig	Configuration for opening task.
PackingTaskConfig
PickAndPlaceColorTaskConfig
PickAndPlaceNextToTaskConfig
PickAndPlaceTaskConfig
PickTaskConfig	Configuration for Franka move-to-pose task.

Attributes:

Name	Type	Description
AllTaskConfigs	TypeAlias

AllTaskConfigs module-attribute

AllTaskConfigs: TypeAlias = BaseMujocoTaskConfig | PickTaskConfig | PickAndPlaceTaskConfig | PickAndPlaceColorTaskConfig | PickAndPlaceNextToTaskConfig | PackingTaskConfig | OpeningTaskConfig | DoorOpeningTaskConfig | NavToObjTaskConfig

BaseMujocoTaskConfig

Bases: Config

Base configuration for MuJoCo tasks.

NOTE: If these task config parameters are left to None, they will be sampled by the task sampler. If these task config parameters are not None, their value will take precedence over any parameters sampled by the task sampler and will remain fixed across all simulation tasks sampled by the task sampler.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
model_config
object_poses	dict[str, list[float]] | None
primitive_objects	dict[str, dict]
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

task_cls instance-attribute

task_cls: type | None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

DoorOpeningTaskConfig

Bases: BaseMujocoTaskConfig

Configuration for RBY1 door opening task.

NOTE: If these task config parameters are left to None, they will be sampled by the task sampler. If these task config parameters are not None, their value will take precedence over any parameters sampled by the task sampler and will remain fixed across all simulation tasks sampled by the task sampler.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
additional_tcp_offset_distance	float
additional_tcp_rotation_offset_mat	ndarray
articulated_joint_range	ndarray | None
articulated_joint_reset_state	ndarray | None
door_body_name	str | None
door_open_reward	float
door_openness_threshold	float
model_config
object_poses	dict[str, list[float]] | None
primitive_objects	dict[str, dict]
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool
viz_target_ee	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

additional_tcp_offset_distance class-attribute instance-attribute

additional_tcp_offset_distance: float = 0.03

additional_tcp_rotation_offset_mat class-attribute instance-attribute

additional_tcp_rotation_offset_mat: ndarray = R.from_euler('Y', -90, degrees=True).as_matrix()

articulated_joint_range class-attribute instance-attribute

articulated_joint_range: ndarray | None = None

articulated_joint_reset_state class-attribute instance-attribute

articulated_joint_reset_state: ndarray | None = None

door_body_name class-attribute instance-attribute

door_body_name: str | None = None

door_open_reward class-attribute instance-attribute

door_open_reward: float = 1.0

door_openness_threshold class-attribute instance-attribute

door_openness_threshold: float = 0.67

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

task_cls class-attribute instance-attribute

task_cls: type | None = None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

viz_target_ee class-attribute instance-attribute

viz_target_ee: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

NavToObjTaskConfig

Bases: BaseMujocoTaskConfig

Configuration for RBY1 navigation to object task.

NOTE: If these task config parameters are left to None, they will be sampled by the task sampler. If these task config parameters are not None, their value will take precedence over any parameters sampled by the task sampler and will remain fixed across all simulation tasks sampled by the task sampler. Uses pickup_obj_name for compatibility with EvalTaskSampler.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
enable_rendering	bool
model_config
object_poses	dict[str, list[float]] | None
pickup_obj_candidates	list[str] | None
pickup_obj_category	str | None
pickup_obj_name	str | None
pickup_obj_start_pose	list[float] | None
pickup_obj_synset	str | None
primitive_objects	dict[str, dict]
receptacle_name	str | None
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
require_visibility	bool
robot_base_pose	list[float] | None
seed	int | None
start_obj_name	str | None
succ_pos_threshold	float
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

enable_rendering class-attribute instance-attribute

enable_rendering: bool = True

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

pickup_obj_candidates class-attribute instance-attribute

pickup_obj_candidates: list[str] | None = None

pickup_obj_category class-attribute instance-attribute

pickup_obj_category: str | None = None

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_obj_start_pose class-attribute instance-attribute

pickup_obj_start_pose: list[float] | None = None

pickup_obj_synset class-attribute instance-attribute

pickup_obj_synset: str | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

require_visibility class-attribute instance-attribute

require_visibility: bool = True

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

seed class-attribute instance-attribute

seed: int | None = None

start_obj_name class-attribute instance-attribute

start_obj_name: str | None = None

succ_pos_threshold class-attribute instance-attribute

succ_pos_threshold: float = 1.5

task_cls class-attribute instance-attribute

task_cls: type | None = None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

OpeningTaskConfig

Bases: PickTaskConfig

Configuration for opening task.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
any_inst_of_category	bool
articulation_object_name	str | None
enable_rendering	bool
joint_goal_position	float | None
joint_index	int | None
joint_name	str | None
joint_start_position	float | None
model_config
object_poses	dict[str, list[float]] | None
packing_object_names	list[str] | None
pickup_obj_goal_pose	list[float] | None
pickup_obj_name	str | None
pickup_obj_start_pose	list[float] | None
place_target_name	str | None
primitive_objects	dict[str, dict]
receptacle_name	str | None
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
scene_settle_duration	float
succ_pos_threshold	float
task_cls	type | None
task_success_threshold	float
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

any_inst_of_category class-attribute instance-attribute

any_inst_of_category: bool = False

articulation_object_name class-attribute instance-attribute

articulation_object_name: str | None = None

enable_rendering class-attribute instance-attribute

enable_rendering: bool = True

joint_goal_position class-attribute instance-attribute

joint_goal_position: float | None = None

joint_index class-attribute instance-attribute

joint_index: int | None = 0

joint_name class-attribute instance-attribute

joint_name: str | None = None

joint_start_position class-attribute instance-attribute

joint_start_position: float | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

packing_object_names class-attribute instance-attribute

packing_object_names: list[str] | None = None

pickup_obj_goal_pose class-attribute instance-attribute

pickup_obj_goal_pose: list[float] | None = None

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_obj_start_pose class-attribute instance-attribute

pickup_obj_start_pose: list[float] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

scene_settle_duration class-attribute instance-attribute

scene_settle_duration: float = 0.0

succ_pos_threshold class-attribute instance-attribute

succ_pos_threshold: float = 0.01

task_cls class-attribute instance-attribute

task_cls: type | None = None

task_success_threshold class-attribute instance-attribute

task_success_threshold: float = 0.2

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PackingTaskConfig

Bases: PickAndPlaceTaskConfig

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
carry_forward_rel_pos_threshold	float
carry_forward_rel_rot_threshold	float
enable_rendering	bool
max_place_receptacle_pos_displacement	float
max_place_receptacle_rot_displacement	float
model_config
object_poses	dict[str, list[float]] | None
packing_object_names	list[str] | None
pickup_obj_goal_pose	list[float] | None
pickup_obj_name	str | None
pickup_obj_start_pose	list[float] | None
place_receptacle_name	str | None
place_receptacle_start_pose	list[float] | None
place_target_name	str | None
primitive_objects	dict[str, dict]
receptacle_name	str | None
receptacle_supported_weight_frac	float
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
scene_settle_duration	float
succ_pos_threshold	float
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

carry_forward_rel_pos_threshold class-attribute instance-attribute

carry_forward_rel_pos_threshold: float = 0.005

carry_forward_rel_rot_threshold class-attribute instance-attribute

carry_forward_rel_rot_threshold: float = np.radians(10)

enable_rendering class-attribute instance-attribute

enable_rendering: bool = True

max_place_receptacle_pos_displacement class-attribute instance-attribute

max_place_receptacle_pos_displacement: float = 0.1

max_place_receptacle_rot_displacement class-attribute instance-attribute

max_place_receptacle_rot_displacement: float = np.radians(45)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

packing_object_names class-attribute instance-attribute

packing_object_names: list[str] | None = None

pickup_obj_goal_pose class-attribute instance-attribute

pickup_obj_goal_pose: list[float] | None = None

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_obj_start_pose class-attribute instance-attribute

pickup_obj_start_pose: list[float] | None = None

place_receptacle_name class-attribute instance-attribute

place_receptacle_name: str | None = None

place_receptacle_start_pose class-attribute instance-attribute

place_receptacle_start_pose: list[float] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_supported_weight_frac class-attribute instance-attribute

receptacle_supported_weight_frac: float = 0.5

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

scene_settle_duration class-attribute instance-attribute

scene_settle_duration: float = 0.0

succ_pos_threshold class-attribute instance-attribute

succ_pos_threshold: float = np.inf

task_cls class-attribute instance-attribute

task_cls: type | None = None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceColorTaskConfig

Bases: PickAndPlaceTaskConfig

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
carry_forward_rel_pos_threshold	float
carry_forward_rel_rot_threshold	float
enable_rendering	bool
max_place_receptacle_pos_displacement	float
max_place_receptacle_rot_displacement	float
model_config
object_colors	dict[str, list[float]] | None
object_poses	dict[str, list[float]] | None
other_receptacle_names	list[str] | None
other_receptacle_start_poses	dict[str, list[float]] | None
packing_object_names	list[str] | None
pickup_obj_goal_pose	list[float] | None
pickup_obj_name	str | None
pickup_obj_start_pose	list[float] | None
place_receptacle_name	str | None
place_receptacle_start_pose	list[float] | None
place_target_name	str | None
primitive_objects	dict[str, dict]
receptacle_name	str | None
receptacle_supported_weight_frac	float
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
scene_settle_duration	float
succ_pos_threshold	float
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

carry_forward_rel_pos_threshold class-attribute instance-attribute

carry_forward_rel_pos_threshold: float = 0.005

carry_forward_rel_rot_threshold class-attribute instance-attribute

carry_forward_rel_rot_threshold: float = np.radians(10)

enable_rendering class-attribute instance-attribute

enable_rendering: bool = True

max_place_receptacle_pos_displacement class-attribute instance-attribute

max_place_receptacle_pos_displacement: float = 0.1

max_place_receptacle_rot_displacement class-attribute instance-attribute

max_place_receptacle_rot_displacement: float = np.radians(45)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_colors class-attribute instance-attribute

object_colors: dict[str, list[float]] | None = None

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

other_receptacle_names class-attribute instance-attribute

other_receptacle_names: list[str] | None = None

other_receptacle_start_poses class-attribute instance-attribute

other_receptacle_start_poses: dict[str, list[float]] | None = None

packing_object_names class-attribute instance-attribute

packing_object_names: list[str] | None = None

pickup_obj_goal_pose class-attribute instance-attribute

pickup_obj_goal_pose: list[float] | None = None

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_obj_start_pose class-attribute instance-attribute

pickup_obj_start_pose: list[float] | None = None

place_receptacle_name class-attribute instance-attribute

place_receptacle_name: str | None = None

place_receptacle_start_pose class-attribute instance-attribute

place_receptacle_start_pose: list[float] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_supported_weight_frac class-attribute instance-attribute

receptacle_supported_weight_frac: float = 0.5

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

scene_settle_duration class-attribute instance-attribute

scene_settle_duration: float = 0.0

succ_pos_threshold class-attribute instance-attribute

succ_pos_threshold: float = np.inf

task_cls class-attribute instance-attribute

task_cls: type | None = None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceNextToTaskConfig

Bases: PickAndPlaceTaskConfig

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
carry_forward_rel_pos_threshold	float
carry_forward_rel_rot_threshold	float
enable_rendering	bool
max_place_receptacle_pos_displacement	float
max_place_receptacle_rot_displacement	float
max_surface_to_surface_gap	float
min_surface_to_surface_gap	float
model_config
object_poses	dict[str, list[float]] | None
packing_object_names	list[str] | None
pickup_obj_goal_pose	list[float] | None
pickup_obj_name	str | None
pickup_obj_start_pose	list[float] | None
place_receptacle_name	str | None
place_receptacle_start_pose	list[float] | None
place_target_name	str | None
primitive_objects	dict[str, dict]
receptacle_name	str | None
receptacle_supported_weight_frac	float
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
scene_settle_duration	float
succ_pos_threshold	float
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

carry_forward_rel_pos_threshold class-attribute instance-attribute

carry_forward_rel_pos_threshold: float = 0.005

carry_forward_rel_rot_threshold class-attribute instance-attribute

carry_forward_rel_rot_threshold: float = np.radians(10)

enable_rendering class-attribute instance-attribute

enable_rendering: bool = True

max_place_receptacle_pos_displacement class-attribute instance-attribute

max_place_receptacle_pos_displacement: float = 0.05

max_place_receptacle_rot_displacement class-attribute instance-attribute

max_place_receptacle_rot_displacement: float = np.radians(45)

max_surface_to_surface_gap class-attribute instance-attribute

max_surface_to_surface_gap: float = 0.05

min_surface_to_surface_gap class-attribute instance-attribute

min_surface_to_surface_gap: float = 0

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

packing_object_names class-attribute instance-attribute

packing_object_names: list[str] | None = None

pickup_obj_goal_pose class-attribute instance-attribute

pickup_obj_goal_pose: list[float] | None = None

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_obj_start_pose class-attribute instance-attribute

pickup_obj_start_pose: list[float] | None = None

place_receptacle_name class-attribute instance-attribute

place_receptacle_name: str | None = None

place_receptacle_start_pose class-attribute instance-attribute

place_receptacle_start_pose: list[float] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_supported_weight_frac class-attribute instance-attribute

receptacle_supported_weight_frac: float = 0.5

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

scene_settle_duration class-attribute instance-attribute

scene_settle_duration: float = 0.0

succ_pos_threshold class-attribute instance-attribute

succ_pos_threshold: float = np.inf

task_cls class-attribute instance-attribute

task_cls: type | None = None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceTaskConfig

Bases: PickTaskConfig

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
carry_forward_rel_pos_threshold	float
carry_forward_rel_rot_threshold	float
enable_rendering	bool
max_place_receptacle_pos_displacement	float
max_place_receptacle_rot_displacement	float
model_config
object_poses	dict[str, list[float]] | None
packing_object_names	list[str] | None
pickup_obj_goal_pose	list[float] | None
pickup_obj_name	str | None
pickup_obj_start_pose	list[float] | None
place_receptacle_name	str | None
place_receptacle_start_pose	list[float] | None
place_target_name	str | None
primitive_objects	dict[str, dict]
receptacle_name	str | None
receptacle_supported_weight_frac	float
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
scene_settle_duration	float
succ_pos_threshold	float
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

carry_forward_rel_pos_threshold class-attribute instance-attribute

carry_forward_rel_pos_threshold: float = 0.005

carry_forward_rel_rot_threshold class-attribute instance-attribute

carry_forward_rel_rot_threshold: float = np.radians(10)

enable_rendering class-attribute instance-attribute

enable_rendering: bool = True

max_place_receptacle_pos_displacement class-attribute instance-attribute

max_place_receptacle_pos_displacement: float = 0.1

max_place_receptacle_rot_displacement class-attribute instance-attribute

max_place_receptacle_rot_displacement: float = np.radians(45)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

packing_object_names class-attribute instance-attribute

packing_object_names: list[str] | None = None

pickup_obj_goal_pose class-attribute instance-attribute

pickup_obj_goal_pose: list[float] | None = None

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_obj_start_pose class-attribute instance-attribute

pickup_obj_start_pose: list[float] | None = None

place_receptacle_name class-attribute instance-attribute

place_receptacle_name: str | None = None

place_receptacle_start_pose class-attribute instance-attribute

place_receptacle_start_pose: list[float] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_supported_weight_frac class-attribute instance-attribute

receptacle_supported_weight_frac: float = 0.5

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

scene_settle_duration class-attribute instance-attribute

scene_settle_duration: float = 0.0

succ_pos_threshold class-attribute instance-attribute

succ_pos_threshold: float = np.inf

task_cls class-attribute instance-attribute

task_cls: type | None = None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickTaskConfig

Bases: BaseMujocoTaskConfig

Configuration for Franka move-to-pose task.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
action_dtype	str
added_objects	dict[str, Path]
enable_rendering	bool
model_config
object_poses	dict[str, list[float]] | None
packing_object_names	list[str] | None
pickup_obj_goal_pose	list[float] | None
pickup_obj_name	str | None
pickup_obj_start_pose	list[float] | None
place_target_name	str | None
primitive_objects	dict[str, dict]
receptacle_name	str | None
referral_expressions	dict[str, str]
referral_expressions_priority	dict[str, list[tuple[float, float, str]]]
robot_base_pose	list[float] | None
scene_settle_duration	float
succ_pos_threshold	float
task_cls	type | None
tracked_object_names	list[str] | None
use_sensors	bool

action_dtype class-attribute instance-attribute

action_dtype: str = 'float32'

added_objects class-attribute instance-attribute

added_objects: dict[str, Path] = {}

enable_rendering class-attribute instance-attribute

enable_rendering: bool = True

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

object_poses class-attribute instance-attribute

object_poses: dict[str, list[float]] | None = None

packing_object_names class-attribute instance-attribute

packing_object_names: list[str] | None = None

pickup_obj_goal_pose class-attribute instance-attribute

pickup_obj_goal_pose: list[float] | None = None

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_obj_start_pose class-attribute instance-attribute

pickup_obj_start_pose: list[float] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

primitive_objects class-attribute instance-attribute

primitive_objects: dict[str, dict] = {}

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

referral_expressions class-attribute instance-attribute

referral_expressions: dict[str, str] = {}

referral_expressions_priority class-attribute instance-attribute

referral_expressions_priority: dict[str, list[tuple[float, float, str]]] = {}

robot_base_pose class-attribute instance-attribute

robot_base_pose: list[float] | None = None

scene_settle_duration class-attribute instance-attribute

scene_settle_duration: float = 0.0

succ_pos_threshold class-attribute instance-attribute

succ_pos_threshold: float = 0.01

task_cls class-attribute instance-attribute

task_cls: type | None = None

tracked_object_names class-attribute instance-attribute

tracked_object_names: list[str] | None = None

use_sensors class-attribute instance-attribute

use_sensors: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

task_sampler_configs

Task sampler configuration classes for MolmoSpaces experiments.

Classes:

Name	Description
BaseMujocoTaskSamplerConfig	Base configuration for task samplers.
DoorOpeningTaskSamplerConfig	Configuration for RBY1 door opening task sampler.
NavToObjTaskSamplerConfig	Configuration for navigation to object task sampler.
ObjectCentricTaskSamplerConfig
OccupancyMapImpl	Which occupancy-map implementation an env hands back from
OpenTaskSamplerConfig
PackingTaskSamplerConfig
PickAndPlaceColorTaskSamplerConfig	Configuration for pick and place color task sampler.
PickAndPlaceNextToTaskSamplerConfig
PickAndPlaceTaskSamplerConfig
PickTaskSamplerConfig	Configuration for Franka move-to-pose task sampler.
PickWithAvatarsTaskSamplerConfig	PickTaskSamplerConfig plus scattering humanoid avatars (scene
RUMPickTaskSamplerConfig

BaseMujocoTaskSamplerConfig

Bases: Config

Base configuration for task samplers.

A task is sampled based on this configuration.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
articulated_object_regex	str | None
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_batch	int
freeze_non_mobile	bool
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_tasks	int | None
max_total_attempts_multiplier	int
mobile_object_regex	str | None
model_config
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
randomize_dynamics	bool
randomize_lighting	bool
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

house_inds instance-attribute

house_inds: list[int] | None

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_tasks instance-attribute

max_tasks: int | None

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

samples_per_house instance-attribute

samples_per_house: int | None

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size instance-attribute

task_batch_size: int

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

DoorOpeningTaskSamplerConfig

Bases: BaseMujocoTaskSamplerConfig

Configuration for RBY1 door opening task sampler.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
choose_random_door_from_scene	bool
dataset_name	str
door_damping_range	tuple
door_frictionloss_range	tuple
door_stiffness_range	tuple
enable_door_joint_randomization	bool
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_batch	int
fixed_door_name	str | None
freeze_non_mobile	bool
handle_damping_range	tuple
handle_frictionloss_range	tuple
handle_stiffness_range	tuple
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_tasks	float
max_total_attempts_multiplier	int
mobile_object_regex	str | None
model_config
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
random_seed	int | None
randomize_dynamics	bool
randomize_lighting	bool
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
robot_base_pose_noise	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (1.0, 1.5)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

choose_random_door_from_scene class-attribute instance-attribute

choose_random_door_from_scene: bool = True

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

door_damping_range class-attribute instance-attribute

door_damping_range: tuple = (8, 12)

door_frictionloss_range class-attribute instance-attribute

door_frictionloss_range: tuple = (8, 12)

door_stiffness_range class-attribute instance-attribute

door_stiffness_range: tuple = (3, 7)

enable_door_joint_randomization class-attribute instance-attribute

enable_door_joint_randomization: bool = True

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

fixed_door_name class-attribute instance-attribute

fixed_door_name: str | None = None

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

handle_damping_range class-attribute instance-attribute

handle_damping_range: tuple = (80, 120)

handle_frictionloss_range class-attribute instance-attribute

handle_frictionloss_range: tuple = (40, 60)

handle_stiffness_range class-attribute instance-attribute

handle_stiffness_range: tuple = (200, 300)

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 22))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

random_seed class-attribute instance-attribute

random_seed: int | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

robot_base_pose_noise class-attribute instance-attribute

robot_base_pose_noise: float = 0.1

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = 0.25

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.7

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 1

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

NavToObjTaskSamplerConfig

Bases: ObjectCentricTaskSamplerConfig

Configuration for navigation to object task sampler. Uses pickup_types/pickup_obj_name for compatibility with EvalTaskSampler.

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_batch	int
eval_without_obj_pos_changes	bool
face_target	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_robot_placement_attempts	int
max_start_object_dist	float
max_tasks	float
max_total_attempts_multiplier	int
max_valid_candidates	int
min_start_object_dist	float
mobile_object_regex	str | None
model_config
num_trajectory_obstacles	int
objaverse_oversampling_factor	int
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
randomize_dynamics	bool
randomize_lighting	bool
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
robot_object_z_offset	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
sample_trajectory_obstacles	bool
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
start_near_object_probability	float
start_object_sampling_radius_range	tuple[float, float]
task_batch_size	int
task_sampler_class	type | None
trajectory_obstacle_drop_height	float
trajectory_obstacle_lateral_jitter	float
trajectory_obstacle_max_frac	float
trajectory_obstacle_min_frac	float
trajectory_obstacle_placement_radius	float
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (1.0, 10.0)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

face_target class-attribute instance-attribute

face_target: bool = True

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = []

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_start_object_dist class-attribute instance-attribute

max_start_object_dist: float = 5.0

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

max_valid_candidates class-attribute instance-attribute

max_valid_candidates: int = 6

min_start_object_dist class-attribute instance-attribute

min_start_object_dist: float = 2.0

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_trajectory_obstacles class-attribute instance-attribute

num_trajectory_obstacles: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = 0.1

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.3

sample_trajectory_obstacles class-attribute instance-attribute

sample_trajectory_obstacles: bool = False

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 1

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

start_near_object_probability class-attribute instance-attribute

start_near_object_probability: float = 0.0

start_object_sampling_radius_range class-attribute instance-attribute

start_object_sampling_radius_range: tuple[float, float] = (0.5, 1.5)

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

trajectory_obstacle_drop_height class-attribute instance-attribute

trajectory_obstacle_drop_height: float = 0.1

trajectory_obstacle_lateral_jitter class-attribute instance-attribute

trajectory_obstacle_lateral_jitter: float = 0.5

trajectory_obstacle_max_frac class-attribute instance-attribute

trajectory_obstacle_max_frac: float = 0.85

trajectory_obstacle_min_frac class-attribute instance-attribute

trajectory_obstacle_min_frac: float = 0.15

trajectory_obstacle_placement_radius class-attribute instance-attribute

trajectory_obstacle_placement_radius: float = 0.15

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

ObjectCentricTaskSamplerConfig

Bases: BaseMujocoTaskSamplerConfig

Methods:

Name	Description
from_dict
load_from_json
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
articulated_object_regex	str | None
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_batch	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_tasks	int | None
max_total_attempts_multiplier	int
mobile_object_regex	str | None
model_config
objaverse_oversampling_factor	int
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
randomize_dynamics	bool
randomize_lighting	bool
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds instance-attribute

house_inds: list[int] | None

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_tasks instance-attribute

max_tasks: int | None

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

samples_per_house instance-attribute

samples_per_house: int | None

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size instance-attribute

task_batch_size: int

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

OccupancyMapImpl

Bases: StrEnum

Which occupancy-map implementation an env hands back from get_occupancy_map().

THOR utils/scene_maps.ProcTHORMap / iTHORMap -- molmo_spaces' own, the default for every task and robot. has a room map (room_ids_to_name, get_free_points_by_room, room-scoped label_at). AABB utils/scene_maps_aabb.AABBMap -- from the FetchMan (g1_molmo) repo. Mostly 99% similar to THORMap, slighlty more permissive in floor labeling and slighly faster.

Attributes:

Name	Type	Description
AABB
THOR

AABB class-attribute instance-attribute

AABB = 'aabb'

THOR class-attribute instance-attribute

THOR = 'thor'

OpenTaskSamplerConfig

Bases: PickTaskSamplerConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_object_placement_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_clutter_objects	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_radius_range	tuple[float, float]
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
target_initial_state_open_percentage	float
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = ['droid']

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = -1.0

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = 0

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_radius_range class-attribute instance-attribute

robot_placement_radius_range: tuple[float, float] = (0.3, 0.8)

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = 0.25

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

target_initial_state_open_percentage class-attribute instance-attribute

target_initial_state_open_percentage: float = 0

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PackingTaskSamplerConfig

Bases: PickAndPlaceTaskSamplerConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
box_uids	list[str] | None
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
episodes_per_receptacle	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_object_placement_attempts	int
max_object_to_receptacle_dist	float
max_place_receptacle_sampling_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_place_receptacle_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_object_to_receptacle_dist	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_clutter_objects	int
num_place_receptacles	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_receptacle_namespace	str
place_receptacle_types	list[str]
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

box_uids class-attribute instance-attribute

box_uids: list[str] | None = None

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

episodes_per_receptacle class-attribute instance-attribute

episodes_per_receptacle: int = 2

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_object_to_receptacle_dist class-attribute instance-attribute

max_object_to_receptacle_dist: float = 0.5

max_place_receptacle_sampling_attempts class-attribute instance-attribute

max_place_receptacle_sampling_attempts: int = 100

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_place_receptacle_dist class-attribute instance-attribute

max_robot_to_place_receptacle_dist: float = 0.7

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_object_to_receptacle_dist class-attribute instance-attribute

min_object_to_receptacle_dist: float = 0.15

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

num_place_receptacles class-attribute instance-attribute

num_place_receptacles: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_receptacle_namespace class-attribute instance-attribute

place_receptacle_namespace: str = 'place_receptacle/'

place_receptacle_types class-attribute instance-attribute

place_receptacle_types: list[str] = []

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = -0.75

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0.25

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = -0.3

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceColorTaskSamplerConfig

Bases: PickAndPlaceTaskSamplerConfig

Configuration for pick and place color task sampler.

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
episodes_per_receptacle	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_object_placement_attempts	int
max_object_to_receptacle_dist	float
max_place_receptacle_sampling_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_place_receptacle_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_object_to_receptacle_dist	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_clutter_objects	int
num_place_receptacles	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_receptacle_namespace	str
place_receptacle_types	list[str]
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

episodes_per_receptacle class-attribute instance-attribute

episodes_per_receptacle: int = 2

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_object_to_receptacle_dist class-attribute instance-attribute

max_object_to_receptacle_dist: float = 0.5

max_place_receptacle_sampling_attempts class-attribute instance-attribute

max_place_receptacle_sampling_attempts: int = 100

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_place_receptacle_dist class-attribute instance-attribute

max_robot_to_place_receptacle_dist: float = 0.7

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_object_to_receptacle_dist class-attribute instance-attribute

min_object_to_receptacle_dist: float = 0.15

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

num_place_receptacles class-attribute instance-attribute

num_place_receptacles: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_receptacle_namespace class-attribute instance-attribute

place_receptacle_namespace: str = 'place_receptacle/'

place_receptacle_types class-attribute instance-attribute

place_receptacle_types: list[str] = []

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = -0.75

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0.25

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = -0.3

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceNextToTaskSamplerConfig

Bases: PickAndPlaceTaskSamplerConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
episodes_per_receptacle	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_object_placement_attempts	int
max_object_to_receptacle_dist	float
max_place_receptacle_sampling_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_place_receptacle_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_object_to_receptacle_dist	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_clutter_objects	int
num_place_receptacles	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_receptacle_namespace	str
place_receptacle_types	list[str]
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

episodes_per_receptacle class-attribute instance-attribute

episodes_per_receptacle: int = 0

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_object_to_receptacle_dist class-attribute instance-attribute

max_object_to_receptacle_dist: float = 0.5

max_place_receptacle_sampling_attempts class-attribute instance-attribute

max_place_receptacle_sampling_attempts: int = 100

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_place_receptacle_dist class-attribute instance-attribute

max_robot_to_place_receptacle_dist: float = 0.7

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_object_to_receptacle_dist class-attribute instance-attribute

min_object_to_receptacle_dist: float = 0.3

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

num_place_receptacles class-attribute instance-attribute

num_place_receptacles: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_receptacle_namespace class-attribute instance-attribute

place_receptacle_namespace: str = 'place_receptacle/'

place_receptacle_types class-attribute instance-attribute

place_receptacle_types: list[str] = []

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = -0.75

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0.25

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = -0.3

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickAndPlaceTaskSamplerConfig

Bases: PickTaskSamplerConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
episodes_per_receptacle	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_object_placement_attempts	int
max_object_to_receptacle_dist	float
max_place_receptacle_sampling_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_place_receptacle_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_object_to_receptacle_dist	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_clutter_objects	int
num_place_receptacles	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_receptacle_namespace	str
place_receptacle_types	list[str]
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

episodes_per_receptacle class-attribute instance-attribute

episodes_per_receptacle: int = 2

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_object_to_receptacle_dist class-attribute instance-attribute

max_object_to_receptacle_dist: float = 0.5

max_place_receptacle_sampling_attempts class-attribute instance-attribute

max_place_receptacle_sampling_attempts: int = 100

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_place_receptacle_dist class-attribute instance-attribute

max_robot_to_place_receptacle_dist: float = 0.7

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_object_to_receptacle_dist class-attribute instance-attribute

min_object_to_receptacle_dist: float = 0.15

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

num_place_receptacles class-attribute instance-attribute

num_place_receptacles: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_receptacle_namespace class-attribute instance-attribute

place_receptacle_namespace: str = 'place_receptacle/'

place_receptacle_types class-attribute instance-attribute

place_receptacle_types: list[str] = []

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = -0.75

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0.25

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = -0.3

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickTaskSamplerConfig

Bases: ObjectCentricTaskSamplerConfig

Configuration for Franka move-to-pose task sampler.

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_object_placement_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_clutter_objects	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = -0.75

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0.25

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = -0.3

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

PickWithAvatarsTaskSamplerConfig

Bases: PickTaskSamplerConfig

PickTaskSamplerConfig plus scattering humanoid avatars (scene population / soft obstacles) around a Pick episode, placed via the same occupancy-map machinery used for robot placement.

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
avatar_agent_radius	float
avatar_articulated	bool
avatar_min_spacing	float
avatar_namespace	str
avatar_placement_radius	float
avatar_uids	list[str] | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_avatar_point_attempts	int
max_object_placement_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_avatars	int
num_clutter_objects	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

avatar_agent_radius class-attribute instance-attribute

avatar_agent_radius: float = 0.3

avatar_articulated class-attribute instance-attribute

avatar_articulated: bool = True

avatar_min_spacing class-attribute instance-attribute

avatar_min_spacing: float = 0.6

avatar_namespace class-attribute instance-attribute

avatar_namespace: str = 'avatar/'

avatar_placement_radius class-attribute instance-attribute

avatar_placement_radius: float = 0.15

avatar_uids class-attribute instance-attribute

avatar_uids: list[str] | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_avatar_point_attempts class-attribute instance-attribute

max_avatar_point_attempts: int = 20

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_avatars class-attribute instance-attribute

num_avatars: int = 10

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = -0.75

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0.25

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = -0.3

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")

RUMPickTaskSamplerConfig

Bases: PickTaskSamplerConfig

Methods:

Name	Description
from_dict
load_from_json
model_post_init
save_to_json
to_dict
to_json

Attributes:

Name	Type	Description
add_grasp_probe	bool
added_pickup_class_max_uids	int | None
added_pickup_class_rank	int | list[int] | None
added_pickup_namespace	str
added_pickup_objects	list[str] | None
articulated_object_regex	str | None
base_pose_sampling_radius_range	tuple[float, float]
cap_freejoint_damping	bool
check_robot_placement_visibility	bool
clutter_covering	bool
clutter_scene_around_target_object	bool
clutter_with_same_class_objects	bool
clutter_with_semantically_similar_objects	bool
clutter_with_taller_objects	bool
dataset_name	str
enable_scene_sleep	bool
enable_texture_randomization	bool
episodes_per_added_pickup	int
episodes_per_batch	int
eval_without_obj_pos_changes	bool
filter_for_grasps	bool
freeze_non_mobile	bool
grasp_libraries	list[str] | None
house_inds	list[int] | None
house_variant	str
license_policy	LicensePolicy
max_added_pickup_placement_attempts	int
max_allowed_sequential_irrecoverable_failures	int
max_allowed_sequential_rollout_failures	int
max_allowed_sequential_task_sampler_failures	int
max_asset_failures	int
max_object_placement_attempts	int
max_receptacle_attempts	int
max_reference_to_added_pickup_dist	float
max_robot_placement_attempts	int
max_robot_to_added_pickup_dist	float
max_robot_to_obj_dist	float
max_robot_to_target_dist	float
max_tasks	float
max_total_attempts_multiplier	int
min_object_separation	float
min_reference_to_added_pickup_dist	float
mobile_object_regex	str | None
model_config
num_added_pickups	int
num_clutter_objects	int
objaverse_oversampling_factor	int
object_placement_radius_range	tuple[float, float]
occupancy_map_cache_size	int
occupancy_map_impl	OccupancyMapImpl
pickup_obj_name	str | None
pickup_types	list[str] | None
place_target_name	str | None
placement_volume_name	str | None
randomize_dynamics	bool
randomize_height	bool
randomize_height_favored	float
randomize_height_max	float | None
randomize_height_min	float
randomize_lighting	bool
randomize_robot_height	bool
randomize_robot_height_max	float
randomize_robot_height_min	float
randomize_robot_textures	bool
randomize_textures	bool
randomize_textures_all	bool
receptacle_name	str | None
receptacle_types	list[str]
referral_expression_clip_filter	bool
reset_precheck_grasp	bool
robot_object_z_offset	float
robot_object_z_offset_random_max	float
robot_object_z_offset_random_min	float
robot_placement_exclusion_threshold	float
robot_placement_rotation_range_rad	float
robot_safety_radius	float
samples_per_house	int | None
scene_xml_paths	list[str] | None
sim_settle_timesteps	int
task_batch_size	int
task_sampler_class	type | None
verbose	bool
weld_robot_base	bool

add_grasp_probe class-attribute instance-attribute

add_grasp_probe: bool = True

added_pickup_class_max_uids class-attribute instance-attribute

added_pickup_class_max_uids: int | None = None

added_pickup_class_rank class-attribute instance-attribute

added_pickup_class_rank: int | list[int] | None = None

added_pickup_namespace class-attribute instance-attribute

added_pickup_namespace: str = 'pickup/'

added_pickup_objects class-attribute instance-attribute

added_pickup_objects: list[str] | None = None

articulated_object_regex class-attribute instance-attribute

articulated_object_regex: str | None = None

base_pose_sampling_radius_range class-attribute instance-attribute

base_pose_sampling_radius_range: tuple[float, float] = (0.0, 0.7)

cap_freejoint_damping class-attribute instance-attribute

cap_freejoint_damping: bool = False

check_robot_placement_visibility class-attribute instance-attribute

check_robot_placement_visibility: bool = True

clutter_covering class-attribute instance-attribute

clutter_covering: bool = False

clutter_scene_around_target_object class-attribute instance-attribute

clutter_scene_around_target_object: bool = False

clutter_with_same_class_objects class-attribute instance-attribute

clutter_with_same_class_objects: bool = False

clutter_with_semantically_similar_objects class-attribute instance-attribute

clutter_with_semantically_similar_objects: bool = False

clutter_with_taller_objects class-attribute instance-attribute

clutter_with_taller_objects: bool = False

dataset_name class-attribute instance-attribute

dataset_name: str = 'procthor-10k'

enable_scene_sleep class-attribute instance-attribute

enable_scene_sleep: bool = True

enable_texture_randomization class-attribute instance-attribute

enable_texture_randomization: bool = False

episodes_per_added_pickup class-attribute instance-attribute

episodes_per_added_pickup: int = 1

episodes_per_batch class-attribute instance-attribute

episodes_per_batch: int = 4

eval_without_obj_pos_changes class-attribute instance-attribute

eval_without_obj_pos_changes: bool = False

filter_for_grasps class-attribute instance-attribute

filter_for_grasps: bool = True

freeze_non_mobile class-attribute instance-attribute

freeze_non_mobile: bool = True

grasp_libraries class-attribute instance-attribute

grasp_libraries: list[str] | None = None

house_inds class-attribute instance-attribute

house_inds: list[int] | None = list(range(0, 4))

house_variant class-attribute instance-attribute

house_variant: str = 'ceiling'

license_policy class-attribute instance-attribute

license_policy: LicensePolicy = LicensePolicy.NONE

max_added_pickup_placement_attempts class-attribute instance-attribute

max_added_pickup_placement_attempts: int = 100

max_allowed_sequential_irrecoverable_failures class-attribute instance-attribute

max_allowed_sequential_irrecoverable_failures: int = 5

max_allowed_sequential_rollout_failures class-attribute instance-attribute

max_allowed_sequential_rollout_failures: int = 10

max_allowed_sequential_task_sampler_failures class-attribute instance-attribute

max_allowed_sequential_task_sampler_failures: int = 10

max_asset_failures class-attribute instance-attribute

max_asset_failures: int = 10

max_object_placement_attempts class-attribute instance-attribute

max_object_placement_attempts: int = 200

max_receptacle_attempts class-attribute instance-attribute

max_receptacle_attempts: int = 10

max_reference_to_added_pickup_dist class-attribute instance-attribute

max_reference_to_added_pickup_dist: float = 0.5

max_robot_placement_attempts class-attribute instance-attribute

max_robot_placement_attempts: int = 10

max_robot_to_added_pickup_dist class-attribute instance-attribute

max_robot_to_added_pickup_dist: float = 0.7

max_robot_to_obj_dist class-attribute instance-attribute

max_robot_to_obj_dist: float = 0.6

max_robot_to_target_dist class-attribute instance-attribute

max_robot_to_target_dist: float = 0.6

max_tasks class-attribute instance-attribute

max_tasks: float = math.inf

max_total_attempts_multiplier class-attribute instance-attribute

max_total_attempts_multiplier: int = 6

min_object_separation class-attribute instance-attribute

min_object_separation: float = 0.05

min_reference_to_added_pickup_dist class-attribute instance-attribute

min_reference_to_added_pickup_dist: float = 0.15

mobile_object_regex class-attribute instance-attribute

mobile_object_regex: str | None = None

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

num_added_pickups class-attribute instance-attribute

num_added_pickups: int = 30

num_clutter_objects class-attribute instance-attribute

num_clutter_objects: int = 3

objaverse_oversampling_factor class-attribute instance-attribute

objaverse_oversampling_factor: int = 30

object_placement_radius_range class-attribute instance-attribute

object_placement_radius_range: tuple[float, float] = (0.1, 0.8)

occupancy_map_cache_size class-attribute instance-attribute

occupancy_map_cache_size: int = 4

occupancy_map_impl class-attribute instance-attribute

occupancy_map_impl: OccupancyMapImpl = OccupancyMapImpl.THOR

pickup_obj_name class-attribute instance-attribute

pickup_obj_name: str | None = None

pickup_types class-attribute instance-attribute

pickup_types: list[str] | None = None

place_target_name class-attribute instance-attribute

place_target_name: str | None = None

placement_volume_name class-attribute instance-attribute

placement_volume_name: str | None = None

randomize_dynamics class-attribute instance-attribute

randomize_dynamics: bool = False

randomize_height class-attribute instance-attribute

randomize_height: bool = False

randomize_height_favored class-attribute instance-attribute

randomize_height_favored: float = 0.95

randomize_height_max class-attribute instance-attribute

randomize_height_max: float | None = None

randomize_height_min class-attribute instance-attribute

randomize_height_min: float = 0.0

randomize_lighting class-attribute instance-attribute

randomize_lighting: bool = False

randomize_robot_height class-attribute instance-attribute

randomize_robot_height: bool = False

randomize_robot_height_max class-attribute instance-attribute

randomize_robot_height_max: float = 0.793

randomize_robot_height_min class-attribute instance-attribute

randomize_robot_height_min: float = 0.7

randomize_robot_textures class-attribute instance-attribute

randomize_robot_textures: bool = False

randomize_textures class-attribute instance-attribute

randomize_textures: bool = False

randomize_textures_all class-attribute instance-attribute

randomize_textures_all: bool = False

receptacle_name class-attribute instance-attribute

receptacle_name: str | None = None

receptacle_types class-attribute instance-attribute

receptacle_types: list[str] = RECEPTACLE_TYPES_THOR

referral_expression_clip_filter class-attribute instance-attribute

referral_expression_clip_filter: bool = False

reset_precheck_grasp class-attribute instance-attribute

reset_precheck_grasp: bool = False

robot_object_z_offset class-attribute instance-attribute

robot_object_z_offset: float = 0

robot_object_z_offset_random_max class-attribute instance-attribute

robot_object_z_offset_random_max: float = 0

robot_object_z_offset_random_min class-attribute instance-attribute

robot_object_z_offset_random_min: float = 0

robot_placement_exclusion_threshold class-attribute instance-attribute

robot_placement_exclusion_threshold: float = 0.15

robot_placement_rotation_range_rad class-attribute instance-attribute

robot_placement_rotation_range_rad: float = math.radians(45)

robot_safety_radius class-attribute instance-attribute

robot_safety_radius: float = 0.15

samples_per_house class-attribute instance-attribute

samples_per_house: int | None = 2

scene_xml_paths class-attribute instance-attribute

scene_xml_paths: list[str] | None = None

sim_settle_timesteps class-attribute instance-attribute

sim_settle_timesteps: int = 500

task_batch_size class-attribute instance-attribute

task_batch_size: int = 1

task_sampler_class class-attribute instance-attribute

task_sampler_class: type | None = None

verbose class-attribute instance-attribute

verbose: bool = False

weld_robot_base class-attribute instance-attribute

weld_robot_base: bool = True

from_dict classmethod

from_dict(data: dict) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def from_dict(cls, data: dict) -> Config:
    return cls.model_validate(data)

load_from_json classmethod

load_from_json(file_path: str) -> Config

Source code in molmo_spaces/configs/abstract_config.py

@classmethod
def load_from_json(cls, file_path: str) -> Config:
    with open(file_path) as f:
        data = f.read()
    return cls.model_validate_json(data)

model_post_init

model_post_init(__context) -> None

Source code in molmo_spaces/configs/task_sampler_configs.py

def model_post_init(self, __context) -> None:
    super().model_post_init(__context)
    if self.added_pickup_class_rank is not None:
        from molmo_spaces.utils.synset_utils import _pickupable_class_ranking

        ranking = _pickupable_class_ranking()
        ranks = (
            self.added_pickup_class_rank
            if isinstance(self.added_pickup_class_rank, list)
            else [self.added_pickup_class_rank]
        )
        all_uids: list[str] = []
        for rank in ranks:
            idx = rank - 1
            if idx < 0 or idx >= len(ranking):
                raise ValueError(
                    f"added_pickup_class_rank={rank} out of range [1, {len(ranking)}]"
                )
            uids = ranking[idx][1]
            if self.added_pickup_class_max_uids is not None:
                uids = uids[: self.added_pickup_class_max_uids]
            all_uids.extend(uids)
        self.added_pickup_objects = all_uids
    if self.added_pickup_objects:
        self.objaverse_oversampling_factor = 1

save_to_json

save_to_json(file_path: str) -> None

Source code in molmo_spaces/configs/abstract_config.py

def save_to_json(self, file_path: str) -> None:
    with open(file_path, "w") as f:
        f.write(self.to_json())

to_dict

to_dict() -> dict

Source code in molmo_spaces/configs/abstract_config.py

def to_dict(self) -> dict:
    return self.model_dump()

to_json

to_json() -> str

Source code in molmo_spaces/configs/abstract_config.py

def to_json(self) -> str:
    # TODO(max): this can cause errors, try printing out the config to see missmatches w/ print(self)
    return self.model_dump_json(warnings="error")