MyoSuite is a collection of musculoskeletal environments and tasks simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API to enable the application of Machine Learning to bio-mechanic control problems.
Full task details | Baselines | Documentation | Tutorials
Below is an overview of the tasks in the MyoSuite.
Getting Started
You will need Python 3.8 or later versions.
It is recommended to use Miniconda and to create a separate environment with:
conda create --name myosuite python=3.8
conda activate myosuite
It is possible to install MyoSuite with:
pip install -U myosuite
for advanced installation, see here.
Test your installation using the following command (this will return also a list of all the current environments):
python -m myosuite.tests.test_myo
You can also visualize the environments with random controls using the command below:
python -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0
NOTE: On MacOS, we moved to mujoco native launch_passive which requires that the Python script be run under mjpython:
mjpython -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0
Examples
It is possible to create and interface with MyoSuite environments just like any other OpenAI gym environments. For example, to use the myoElbowPose1D6MRandom-v0 environment, it is possible simply to run:
from myosuite.utils import gym
env = gym.make('myoElbowPose1D6MRandom-v0')
env.reset()
for _ in range(1000):
env.mj_render()
env.step(env.action_space.sample()) # take a random action
env.close()
You can find tutorials and the ICRA2023 Colab Tutorial
License
MyoSuite is licensed under the Apache License.
Citation
If you find this repository useful in your research, please consider giving a star ⭐ and cite our arXiv paper by using the following BibTeX entrys.
@Misc{MyoSuite2022,
author = {Vittorio, Caggiano AND Huawei, Wang AND Guillaume, Durandau AND Massimo, Sartori AND Vikash, Kumar},
title = {MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control},
publisher = {arXiv},
year = {2022},
howpublished = {\url{https://github.com/myohub/myosuite}},
year = {2022}
doi = {10.48550/ARXIV.2205.13600},
url = {https://arxiv.org/abs/2205.13600},
}
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