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. At this moment, the library has been tested only on MacOs and Linux with MuJoCo v2.1.0.
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: If the visualization results in a GLFW error, this is because mujoco-py does not see some graphics drivers correctly. This can usually be fixed by explicitly loading the correct drivers before running the python script. See this page for details.
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:
import myosuite
import gym
env = gym.make('myoElbowPose1D6MRandom-v0')
env.reset()
for _ in range(1000):
env.sim.render(mode='window')
env.step(env.action_space.sample()) # take a random action
env.close()
You can find tutorials on how to load MyoSuite models/tasks, train them, and visualize their outcome. Also, you can find baselines to test some pre-trained policies.
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/facebookresearch/myosuite}},
year = {2022}
doi = {10.48550/ARXIV.2205.13600},
url = {https://arxiv.org/abs/2205.13600},
}
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file MyoSuite-1.6.1.tar.gz.
File metadata
- Download URL: MyoSuite-1.6.1.tar.gz
- Upload date:
- Size: 36.1 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.2 CPython/3.9.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c159a34d047039c1f5125fceb1c8c3b6fe81a4f843f5289cb16c9c1bf878cdce
|
|
| MD5 |
ce69f5372a3acebead56a9a006fdda2d
|
|
| BLAKE2b-256 |
9ee12b62291ffebd52d2ec0be69cc7c0b7c766fd07935c93efd0ff31e0c8b19f
|
File details
Details for the file MyoSuite-1.6.1-py3-none-any.whl.
File metadata
- Download URL: MyoSuite-1.6.1-py3-none-any.whl
- Upload date:
- Size: 36.2 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.2 CPython/3.9.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f719b39f8b774773e91501a19d28d45c7d65dcbfbd2af7707e3d69e9aea05501
|
|
| MD5 |
8485d51efc59cb9a677ca99bcdd45c25
|
|
| BLAKE2b-256 |
9ddb856e2730c07550a852a2ca62274e72c4f941c8fcc8bba70f501c4e67391a
|
