---

name: "myosuite" description: "Use MyoSuite's MuJoCo musculoskeletal environments, task registry, model and kinematics tools, reference-motion utilities, optional MJX acceleration, rendering, and bounded reinforcement-learning integrations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MyoSuite operating guide

MyoSuite is a Python package of musculoskeletal MuJoCo environments exposed through Gymnasium-compatible APIs. Use this skill for task selection, short control rollouts, observation/reward inspection, simulation and rendering, model edits/IK, reference trajectories, optional JAX/MJX acceleration, or RL integration planning. This is a self-contained operating graph: use its bundled references and scripts, not the source checkout's tests, tutorials, or launchers.

Route first

• Environment/task creation, reset/step, seeds, spaces, task IDs, or safe headless rollouts: read environments.
• MuJoCo model state, XML loading, cameras, offscreen/onscreen rendering, or viewer failures: read simulation-rendering.
• XML/model transformations, robot edits, site-pose IK, or quaternion/vector utilities: read model-editing-kinematics.
• Fixed/random/tracked trajectories, interpolation, playback, or reference logs: read reference-motion.
• JAX/MJX/MJWarp, device probes, CUDA, or NumPy/JAX parity: read mjx-acceleration.
• Saved-policy evaluation, SB3/MJRL/DEP-RL/TorchRL boundaries, or training handoff/config validation: read training-integration.

For package-wide setup failures, read installation and assets and troubleshooting. Read repository provenance before treating this graph as current for a changed checkout.

Install and smoke check

Use an isolated Python 3.10–3.13 environment and install the released package or an editable source distribution with its documented MuJoCo model assets. The core route needs Gymnasium below 1.3, MuJoCo 3.6.x, NumPy, Click, and the other base dependencies declared by the package. Do not install MJX/CUDA or RL extras unless the requested route needs them.

python -m pip install -U myosuite
python -c "import myosuite, mujoco; print(myosuite.__version__, mujoco.__version__)"
python sub-skills/environments/scripts/environment_smoke.py \
  --env-id myoElbowPose1D6MRandom-v0 --steps 3 --seed 123 --render none

For a source checkout, missing MuJoCo include files usually mean the repository's asset submodules/package data are unavailable; see the asset guidance rather than changing task IDs. The verified package snapshot imported MyoSuite 2.12.2, MuJoCo 3.6.0, and Gymnasium 1.2.3, registered 398 environments, and completed a seeded pose reset/step smoke after model assets were available.

Operating rules

1. Import myosuite before querying the Gymnasium registry; registration is an import side effect. Prefer gym.spec(task_id) and an exact task ID from the linked task catalog before gym.make(task_id).
2. Use reset(seed=...), seed the action space for deterministic sampled controls, honor the five-value Gymnasium step result, and close every environment in a finally block.
3. Keep CPU/base behavior separate from optional display and MJX/CUDA claims. A successful MuJoCo CPU step does not prove JAX, CUDA, or a windowing stack.
4. Bound episodes, frames, output files, and training plans. Do not fetch model assets, open viewers, deserialize untrusted policies, or launch long experiments without explicit user intent.
5. Preserve task-specific observation/action shapes and reference joint order; do not copy a control vector or reference file between tasks without checking its declared spaces and dimensions.

Verification boundary

The base package, registry, seeded environment lifecycle, model-editor tests, CLI help, and safe bundled scripts are CPU-verifiable. MJX/JAX/CUDA, onscreen viewer behavior, networked asset setup, and long RL training are optional or environment-dependent; route them to the relevant sub-skill and keep any unverified backend visible. The generated skill is intentionally not imported into the managed DisCo repo-skill collection for this run.