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name: robocasa description: "Guides Researchers through RoboCasa365 embodied-AI simulation, kitchen task and scene selection, demonstration datasets, teleoperation, and safe validation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

RoboCasa

Use this skill for RoboCasa/RoboCasa365 simulation, kitchen manipulation tasks, scene and asset configuration, demonstration datasets, policy-evaluation setup, or interactive collection. It covers the public robocasa package and its robosuite/MuJoCo integration; it does not replace a policy-learning framework.

Fast route

1. Check package readiness first. RoboCasa 1.0.1 asserts MuJoCo 3.3.1, NumPy 2.2.5, and robosuite >=1.5.2. Install the package's declared dependencies and public robosuite separately, then run the diagnostic in simulation-environments.
2. Separate data gates. A successful import or environment constructor does not prove that the external kitchen fixture/object/texture archives or datasets exist. Validate those paths before reset, playback, rendering, or collection; do not download multi-GB data implicitly.
3. Choose the route below. Read only the focused sub-skill and its linked references before producing commands or code.

Install and import gate

Use an isolated Python 3.11 environment when possible. Install a compatible public robosuite release before RoboCasa, then install RoboCasa from its source or release tree:

python -m pip install "robosuite>=1.5.2"
python -m pip install -e .
python -c 'import robocasa, robosuite, mujoco, numpy; print(robocasa.__version__, robosuite.__version__, mujoco.__version__, numpy.__version__)'

Keep the exact package pins together. If import fails, fix version conflicts before investigating assets. Optional MimicGen, GR00T, SpaceMouse, display, and asset-authoring tools are not part of the minimal import gate.

Choose a sub-skill

• simulation-environments — install/import checks, gym.make, create_env, controller and camera choices, split mapping, reset/step data, seeds, rendering, and bounded rollouts.
• tasks-scenes-assets — atomic and composite task selection, kitchen layouts/styles, fixture and object registries, placement configuration, custom tasks, and asset prerequisites.
• datasets-demonstrations — dataset registry metadata and soups, pretrain/target sources, LeRobot and HDF5 structure, inspection, playback, conversion, and download planning.
• teleoperation-and-collection — keyboard/SpaceMouse setup, interactive viewer constraints, demonstration collection, HDF5 handoff, and interruption recovery.

Cross-route rules

• Route split, layout_ids, style_ids, object registries, and fixture/XML failures to the task/scene route; route reset/step and action-shape failures to the simulation route.
• Route registry paths, LeRobot/HDF5 schemas, and playback flags to the dataset route; route live input and HDF5 recording to teleoperation/collection.
• Keep rollouts, downloads, playback, and collection bounded and explicit. Always close environments and preserve a writable output directory.
• Do not tell a future agent to open the original checkout. All reusable diagnostics and recipes are bundled under this skill.

Read troubleshooting for cross-cutting install, data, rendering, and optional-dependency failures. Read repository provenance before deciding whether this graph is current for a repository checkout or should be refreshed.