MolmoSpaces¶
Large-scale assets and benchmarks for vision-language policies.
Overview¶
MolmoSpaces is a large-scale simulation environment and benchmark suite for training and evaluating vision-language policies in robotics. It provides:
- High-fidelity 3D asset libraries (objects, environments, robots)
- Multi-simulator support (MuJoCo, Isaac Sim, ManiSkill)
- Standardized evaluation protocols for vision-language policies
- Data generation pipelines for imitation learning
Quick Links¶
| Resource | Description |
|---|---|
| Assets | Asset management and resource usage |
| Data Format | Episode and observation data specifications |
| Data Processing | Preprocessing and postprocessing pipelines |
| Code Structure | Repository layout and module organization |
| Development | Contributing guidelines and tooling setup |
| API Reference | Auto-generated Python API documentation |
Installation¶
# Clone the repository
git clone https://github.com/allenai/molmospaces.git
cd molmospaces
# Install with uv (recommended)
uv pip install -e ".[mujoco]"
See the README on GitHub for full installation instructions including conda setup and optional dependency groups.