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name: simulation-environments description: "Guides researchers through RoboCasa installation gates, Gymnasium and robosuite environment construction, seeded simulation, observations and actions, rendering, and bounded rollouts." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

RoboCasa simulation environments

Use this route when the task is to install or diagnose RoboCasa, construct a robosuite or Gymnasium environment, choose a pretrain/target/all split, understand reset-step data, check seed behavior, render, or run a short random rollout. Read the focused references before writing environment code:

• API reference — compatibility gates, Kitchen, create_env, the Gym wrapper, split semantics, and action/observation keys.
• Workflows — install/import checks, construction, deterministic probes, render configuration, and bounded video rollouts.
• Troubleshooting — duplicate keyword errors, missing external XML/object assets, backend problems, and action mismatches.
• Safe diagnostic — run from any working directory with no download; it reports package gates, optional assets, and render signals. Use --help first and --require-assets only when a full reset is intended.

Fast route

1. Install the pinned RoboCasa dependencies and public robosuite >=1.5.2 as described in Workflows. Import robocasa only after the exact MuJoCo and NumPy gates pass.
2. Run python path/to/check_install.py --json (or invoke the bundled script from this skill directory) to separate package readiness from downloaded asset readiness. The checkout may import successfully while a reset fails because the large kitchen fixture/object downloads are absent.
3. For the supported Gym recipe, import robocasa before gym.make, use an explicit supported split (pretrain, target, or all), and let create_env own use_camera_obs and has_offscreen_renderer. Do not pass either of those two keywords again through kwargs.
4. Validate the action dictionary against the exact keys in the API reference; do not mix the flat 12-value convert_action input with a Gym action dict.
5. Keep rollouts bounded by num_steps. create_env deliberately sets ignore_done=True; a requested horizon is therefore not a replacement for an outer rollout bound unless the lower-level API is used directly.

Scope boundaries

This route owns simulation lifecycle and package/backend behavior. Route task catalogs, scene/layout/style selection, fixture/object availability, and asset validation through the root skill's tasks-scenes-assets route. Route dataset schemas/playback through datasets-demonstrations, and teleoperation or collection through teleoperation-and-collection. Use the root skill as the cross-sub-skill router: root RoboCasa skill.

Verification status

Package import, exact version assertions, and API/signature inspection were verified for RoboCasa 1.0.1 with MuJoCo 3.3.1, NumPy 2.2.5, robosuite 1.5.2, Gymnasium 0.29.1, h5py 3.16.0, and LeRobot 0.3.3. RoboCasa registers 374 kitchen environments. A direct create_env constructor succeeds in the inspection setup, but reset is not a full simulation pass without the external fixture/object assets; do not claim reset, stepping, rendering, or video success merely from import or constructor success. MimicGen is optional and was not installed.