---

name: "environments" description: "Discover, select, and safely control MyoSuite Gymnasium environments on the base CPU backend." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MyoSuite environments

Use this sub-skill when a user needs to install the base package, identify a registered task, create an environment, run a short reset/step rollout, inspect its spaces or diagnostics, seed it, or close it cleanly. Start with a named registered environment rather than guessing a model filename.

Route the request

• Core import, Gymnasium registration, task IDs, reset/step, spaces, rewards, info, seeding, lifecycle, or safe no-display inspection: handle here.
• Viewer/window behavior and MuJoCo rendering internals: route to simulation-rendering.
• Model XML editing, IK, or task-specific kinematic changes: route to model-editing-kinematics.
• Reference trajectories or playback: route to reference-motion.
• JAX/MJX execution: route to mjx-acceleration; do not imply that a CPU smoke proves MJX or CUDA support.
• Long training commands and learner configuration: route to training-integration.

See the API reference for the full contract, the task catalog for selection, and troubleshooting when setup or assets fail.

Base prerequisites

The supported base route is Python 3.10–3.13 with the package's normal runtime dependencies, including Gymnasium below 1.3, MuJoCo 3.6.x, NumPy, Click, and the package data. The verified inspection combination was Python 3.11, MyoSuite 2.12.2, MuJoCo 3.6.0, and Gymnasium 1.2.3. Install the released package into an isolated environment, or use the project's documented source installation with all required model assets initialized. Do not install the MJX/CUDA extras just to run this core route.

Importing myosuite imports the package's Gym compatibility helper and then registers its task suites. If neither gymnasium nor the legacy gym package is importable, import fails before an environment can be made. Prefer Gymnasium for new code.

Fast path: named, headless rollout

For a standard CPU check, run the bundled safe smoke script:

python sub-skills/environments/scripts/environment_smoke.py \
  --env-id myoElbowPose1D6MRandom-v0 --steps 3 --seed 123 --render none

The script only creates the requested environment, samples bounded actions, uses reset(seed=...), performs bounded steps, prints contract summaries, and always calls close(). It does not open a viewer, evaluate policy files, write videos, fetch assets, or execute arbitrary environment arguments. Add --check-determinism to repeat the short rollout with the same reset and action-space seeds.

The equivalent minimal Python pattern is:

from myosuite.utils import gym

env = gym.make("myoElbowPose1D6MRandom-v0")
try:
    env.action_space.seed(123)
    observation, info = env.reset(seed=123)
    for _ in range(3):
        observation, reward, terminated, truncated, info = env.step(
            env.action_space.sample()
        )
        if terminated or truncated:
            break
finally:
    env.close()

Do not call mj_render() in a no-display workflow. gym.make normally returns Gymnasium wrappers; use env.unwrapped only when the user explicitly needs MyoSuite internals such as get_obs_dict, get_reward_dict, get_proprioception, get_exteroception, horizon, or get_env_state.

Discover before creating

1. Import myosuite so registration side effects have run.
2. Prefer an exact ID from the task catalog.
3. Check gym.spec(task_id) before gym.make(task_id); an unknown ID is a selection error, not an installation fix.
4. Read spec.max_episode_steps for the wrapper horizon. Core registrations include fixed/random tasks and automatically registered Sarc and Fati variants; hand tasks also have Reaf variants.
5. If the name is uncertain, list myosuite.myosuite_env_suite or filter gym.envs.registry.keys() instead of probing arbitrary strings.

A valid registry entry does not prove that every model asset can be loaded. Only gym.make followed by reset establishes that the selected model is usable in the current installation.

Lifecycle and contracts

• env = gym.make(id, **kwargs) creates a wrapped task. Keep construction kwargs limited to documented task options; do not pass untrusted strings to an evaluator.
• env.reset(seed=seed) returns (observation, info) under Gymnasium. Seed the action space separately with env.action_space.seed(seed) when comparing random-policy rollouts. A legacy Gym installation may return only an observation; follow the installed API rather than unpacking blindly.
• env.step(action) returns (observation, reward, terminated, truncated, info). Stop on either boolean; the wrapper enforces the registered time limit through truncated, while a task's reward dictionary can produce terminated.
• action must fit env.action_space, normally a float32 Box. Sampling from action_space is safer than hand-building controls. Myo muscle tasks may internally map normalized controls before MuJoCo advances.
• observation is normally a flat NumPy vector in observation_space. Its task-specific fields can be inspected through the unwrapped task's obs_dict; do not assume all tasks expose the same keys or dimension.
• reward is the configured dense reward scalar by default. info contains MyoSuite diagnostics such as time, rwd_dense, rwd_sparse, solved, done, obs_dict, rwd_dict, proprio_dict, visual_dict, and state. Treat nested arrays as owned by the environment and copy them before storing.
• Always call env.close() in finally, including after failed reset, step, or inspection. Avoid creating a viewer merely to inspect spaces.

Safe inspection

Use environment_smoke.py --list for a registry snapshot and python -m myosuite.utils.examine_env --help to inspect the supported CLI surface. The upstream CLI can run random or supplied policies and supports --render none, but it also supports output/video options and policy loading; use it only with trusted inputs and bounded episode counts. The bundled smoke script is preferred for automated checks.

For deeper CPU inspection, after a successful reset/step use only public methods: env.unwrapped.get_obs_dict(...), get_reward_dict(...), get_proprioception(), get_exteroception(), get_env_infos(), and get_env_state(). Route image capture or viewer setup away from this skill.

Verification evidence and limits

The base import and a myoElbowPose1D6MRandom-v0 reset/step smoke passed in the private Python 3.11 inspection environment after the repository's five MuJoCo asset submodules were initialized. The source README/minimal example, package initializers, environment base, Myo base/task registrations, task catalog, core environment tests, and examine_env --help define the claims in the linked references. Native candidates for later verification are a single pose reset/step lifecycle, selected core checks from test_myo/test_envs, and examine_env --help. Optional MJX/JAX/CUDA and display-backed rendering remain separate, explicitly unverified routes.