MyoSuite 3.0.0

MyoSuite is a collection of musculoskeletal environments and tasks simulated with the MuJoCo physics engine. It serves researchers and practitioners across biomechanics, neuroscience, machine learning, sports medicine, and physical rehabilitation.

Documentation · Tutorials · Task list

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Install

Python 3.10–3.14 is supported (the GPU [mjlab] extra needs Python ≤3.13). From source (recommended):

git clone https://github.com/MyoHub/myosuite.git
cd myosuite
pip install -e ".[rl]"          # CPU training (Stable-Baselines3)
# uv pip install -e ".[mjlab]" --torch-backend=auto   # GPU (NVIDIA, Linux or Windows), picks the torch
                                 # build for your driver; with pip see docs/source/install.rst

Or: uv sync -p 3.10 --extra rl.

From PyPI: pip install -U myosuite.

Models. The musculoskeletal models ship in the myo-sim package, pinned to 0.2.3 and installed from PyPI together with MyoSuite, so no git submodules and no separate model install are needed. The few MPL, YCB and furniture assets the tasks use are bundled in myosuite/envs/myo/assets/. Environments compose their models with ModelBuilder (see the model builder guide). A different myo-sim version can change joints and ranges (the thumb joints did in 0.2.0), so the baseline policies are only valid for the pinned version.

Verify (replace "onscreen" with "offscreen" when running on a remote, headless machine):

python -c "import myosuite; print(len(myosuite.myosuite_env_suite), 'envs')"
python -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0 --render onscreen
# macOS viewer: mjpython -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0 --render onscreen

Run these from a directory that does not directly contain a folder named myosuite (for example not the parent of your clone); otherwise Python imports that folder as a namespace package and fails with cannot import name ... from 'myosuite' (unknown location).

Quick start

from myosuite import make_env

env = make_env("myoElbowPose1D6MRandom-v0")
obs, info = env.reset(seed=0)
for _ in range(1000):
    obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
    if terminated or truncated:
        obs, info = env.reset()
env.close()

info["obs_dict"] and info["rwd_dict"] break down the observation and reward every step.

Train on CPU:

from stable_baselines3 import PPO
from myosuite import make_env

env = make_env("myoElbowPose1D6MRandom-v0")
model = PPO("MlpPolicy", env, device="cpu")
model.learn(total_timesteps=100_000)

Or use the ready-made script (PPO, SAC or TD3; saves the model and scores the policy at the end):

python scripts/train_sb3.py myoElbowPose1D6MRandom-v0 --timesteps 500000

Train on GPU (same env_id, mjlab / RSL-RL):

python scripts/train_mjlab.py myoElbowPose1D6MRandom-v0 --env.scene.num-envs 1024

No step count is needed: it runs the task's default number of PPO iterations (--agent.max-iterations, 1000 unless the task sets another) and stops early once the deterministic policy reaches 95% success (--stop-on-success False turns that off).

The same call builds either backend, and an EnvConfig overrides the registered defaults (episode length, control step, muscle-command features such as motor noise and fatigue) for both:

from myosuite import make_env
from myosuite.core.config import EnvConfig
from myosuite.envs.wrappers import FatigueWrapper, MotorNoiseWrapper

env_cpu = make_env("myoElbowPose1D6MRandom-v0")                               # CPU, registered defaults
env_gpu = make_env("myoElbowPose1D6MRandom-v0", backend="mjlab", num_envs=1024)  # GPU twin (needs the mjlab extra)

cfg = EnvConfig(
    "myoElbowPose1D6MRandom-v0",
    max_episode_steps=300,
    features=((MotorNoiseWrapper, {"motor_noise": {"constant_std": 0.05}}), FatigueWrapper),
)
env_cpu = make_env(cfg)                               # CPU with noise and fatigue
env_gpu = make_env(cfg, backend="mjlab", num_envs=1024)  # the same on the GPU

Muscle-condition variants of an env are separate ids with a prefix: myoSarcElbowPose1D6MRandom-v0 (sarcopenia), myoFatiElbowPose1D6MRandom-v0 (fatigue), myoReafHandPoseRandom-v0 (reafferentation). Each is the base env registered with the matching wrapper; to use these features (noise, fatigue, ...) on any env, or to register your own variant, see the neuroscience guide (examples) and the cross-backend contract (how features are registered and which envs and backends support them).

Environments

List every registered CPU ID: python -c "import myosuite; print('\n'.join(myosuite.myosuite_env_suite))". Annotated catalog: environments.

Backends

The mjlab backend needs pip install -e ".[mjlab]". make_env only builds the env (to step it, evaluate a policy or write your own training loop); scripts/train_sb3.py (CPU) and scripts/train_mjlab.py (GPU) are the ready-made training scripts.

A task’s CPU and mjlab halves share one env_id (see cross-backend contract). make_env(EnvConfig(env_id, backend=..., features=...)) builds either half with the same episode length, control step and muscle-command features. An MJX (JAX) path also exists; it is experimental and not the supported training route.

Tutorials

All notebooks are listed, with what each needs, in the tutorial index.

Start with 1.1 Get Started, then 2.1 Train SB3 Policy (CPU).

GPU walk-through: 2.2 Train MjLab Policy.

Full-body MuscleMimic: 5.1 Load Policy (playback) and 5.3 Train with mjlab; background, data and checkpoints in the MuscleMimic README.

License

Apache License.

Citation

If you use MyoSuite, please cite the MyoSuite paper:

@inproceedings{Caggiano2022MyoSuite,
  title     = {{MyoSuite} -- A contact-rich simulation suite for musculoskeletal motor control},
  author    = {Caggiano, Vittorio and Wang, Huawei and Durandau, Guillaume and Sartori, Massimo and Kumar, Vikash},
  booktitle = {Learning for Dynamics and Control (L4DC)},
  year      = {2022},
  doi       = {10.48550/ARXIV.2205.13600},
  url       = {https://arxiv.org/abs/2205.13600},
}

For MyoSuite 3 (the GPU backends, the muscle conditions, the MyoChallenge envs), cite it as software:

@misc{MyoSuite2026,
  title        = {{MyoSuite} 3.0 -- A multimodal platform for efficient and scalable musculoskeletal motor control},
  author       = {Caggiano, Vittorio and Hodossy, Balint and Fischer, Florian and Wang, Cheryl and {MyoSuite Team}},
  year         = {2026},
  howpublished = {\url{https://github.com/myohub/myosuite}},
}

If you use the full-body MuscleMimic models, checkpoints, retargeted motions or tutorials 5.1-5.5, also cite:

@article{Li2026MuscleMimic,
  title   = {Towards Embodied AI with {MuscleMimic}: Unlocking full-body musculoskeletal motor learning at scale},
  author  = {Li, Chengkun and Wang, Cheryl and Ziliotto, Bianca and Simos, Merkourios and Kovecses, Jozsef and Durandau, Guillaume and Mathis, Alexander},
  journal = {arXiv preprint arXiv:2603.25544},
  year    = {2026},
}

The motion data behind MuscleMimic comes from AMASS (Mahmood et al., ICCV 2019), whose KIT subset (Mandery et al., 2015, 2016) provides the locomotion clips, retargeted with GMR (Araújo et al., 2025). The retargeted datasets are for non-commercial research under the AMASS license and ask you to cite AMASS and MuscleMimic.

Further papers use or build on MyoSuite: MyoSim (ICRA 2022), MyoDex (ICML 2023), SAR (RSS 2023), MyoChallenge 2022, DEP-RL (ICLR 2023), Lattice (NeurIPS 2023), MyoInteract (DIS 2026, a GUI framework for prototyping biomechanical HCI tasks on top of MyoSuite) and the Virtual Gait Lab for testing prosthetics with simulated users (IEEE T-MRB 2026). The sources behind the models, the muscle fatigue model, the controllers and the motion data are collected in docs/source/references.rst (page References of the documentation); CITATION.cff holds the machine-readable citation.