{"id":13737815,"url":"https://github.com/MyoHub/myosuite","last_synced_at":"2025-05-08T15:31:39.879Z","repository":{"id":37813971,"uuid":"429080848","full_name":"MyoHub/myosuite","owner":"MyoHub","description":"MyoSuite is a collection of environments/tasks to be solved by musculoskeletal models simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API. ","archived":false,"fork":false,"pushed_at":"2025-04-30T09:57:37.000Z","size":346085,"stargazers_count":930,"open_issues_count":38,"forks_count":127,"subscribers_count":27,"default_branch":"main","last_synced_at":"2025-04-30T11:42:07.282Z","etag":null,"topics":["machine-learning","motor-control","mujoco","musculoskeletal"],"latest_commit_sha":null,"homepage":"https://sites.google.com/view/myosuite","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MyoHub.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"docs/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"docs/CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-11-17T14:34:44.000Z","updated_at":"2025-04-30T00:44:23.000Z","dependencies_parsed_at":"2024-02-04T20:36:29.789Z","dependency_job_id":"ea623278-6536-4442-a23b-b77c268d39d9","html_url":"https://github.com/MyoHub/myosuite","commit_stats":null,"previous_names":["myohub/myosuite","facebookresearch/myosuite"],"tags_count":30,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MyoHub%2Fmyosuite","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MyoHub%2Fmyosuite/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MyoHub%2Fmyosuite/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MyoHub%2Fmyosuite/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MyoHub","download_url":"https://codeload.github.com/MyoHub/myosuite/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253096074,"owners_count":21853539,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","motor-control","mujoco","musculoskeletal"],"created_at":"2024-08-03T03:02:01.996Z","updated_at":"2025-05-08T15:31:39.861Z","avatar_url":"https://github.com/MyoHub.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"\u003c!-- =================================================\n# Copyright (c) Facebook, Inc. and its affiliates\nAuthors  :: Vikash Kumar (vikashplus@gmail.com), Vittorio Caggiano (caggiano@gmail.com)\n================================================= --\u003e\n\u003cimg src=\"https://github.com/myohub/myosuite/blob/main/docs/source/images/Full%20Color%20Horizontal%20wider.png?raw=true\" width=800\u003e\n\n[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat\u0026labelColor=005BBB)](https://opensource.facebook.com/support-ukraine)\n[![PyPI](https://img.shields.io/pypi/v/myosuite)](https://pypi.org/project/MyoSuite/)\n[![Documentation Status](https://readthedocs.org/projects/myosuite/badge/?version=latest)](https://myosuite.readthedocs.io/en/latest/)\n![PyPI - License](https://img.shields.io/pypi/l/myosuite)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/myohub/myosuite/blob/main/docs/CONTRIBUTING.md)\n[![Downloads](https://static.pepy.tech/badge/myosuite)](https://pepy.tech/project/myosuite)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zFuNLsrmx42vT4oV8RbnEWtkSJ1xajEo)\n[![Slack](https://img.shields.io/badge/Slack-4A154B?style=for-the-badge\u0026logo=slack\u0026logoColor=white)](https://join.slack.com/t/myosuite/shared_invite/zt-1zkpw2zzk-NhVhVlSDxhoMHbzROD8gMA)\n[![Twitter Follow](https://img.shields.io/twitter/follow/MyoSuite?style=social)](https://twitter.com/MyoSuite)\n\n`MyoSuite` is a collection of musculoskeletal environments and tasks simulated with the [MuJoCo](http://www.mujoco.org/) physics engine and wrapped in the OpenAI ``gym`` API to enable the application of Machine Learning to bio-mechanic control problems.\n\n\n\n[Documentation](https://myosuite.readthedocs.io/en/latest/) | [Tutorials](https://github.com/myohub/myosuite/tree/main/docs/source/tutorials) | [Task specifications](https://github.com/myohub/myosuite/blob/main/docs/source/suite.rst#tasks)\n\n\nBelow is an overview of the tasks in the MyoSuite.\n\n\u003cimg width=\"1240\" alt=\"TasksALL\" src=\"https://github.com/myohub/myosuite/blob/main/docs/source/images/myoSuite_All.png?raw=true\"\u003e\n\n\n\n## Installations\nYou will need Python 3.8 or later versions.\n\nIt is recommended to use [Miniconda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links) and to create a separate environment with:\n``` bash\nconda create --name myosuite python=3.8\nconda activate myosuite\n```\n\nIt is possible to install MyoSuite with:\n``` bash\npip install -U myosuite\n```\nfor advanced installation, see [here](https://myosuite.readthedocs.io/en/latest/install.html#alternative-installing-from-source).\n\nTest your installation using the following command (this will return also a list of all the current environments):\n``` bash\npython -m myosuite.tests.test_myo\n```\n\n\nYou can also visualize the environments with random controls using the command below:\n``` bash\npython -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0\n```\n**NOTE:** On MacOS, we moved to mujoco native `launch_passive` which requires that the Python script be run under `mjpython`:\n``` bash\nmjpython -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0\n```\n\nIt is possible to take advantage of the latest MyoSkeleton. Once added (follow the instructions prompted by `python -m myosuite_init`), run:\n``` bash\npython -m myosuite.utils.examine_sim -s myosuite/simhive/myo_model/myoskeleton/myoskeleton.xml\n```\n\n## Examples\nIt 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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zFuNLsrmx42vT4oV8RbnEWtkSJ1xajEo)\n\n\n\n```python\nfrom myosuite.utils import gym\nenv = gym.make('myoElbowPose1D6MRandom-v0')\nenv.reset()\nfor _ in range(1000):\n  env.mj_render()\n  env.step(env.action_space.sample()) # take a random action\nenv.close()\n```\n\nYou can find our [tutorials](https://github.com/myohub/myosuite/tree/main/docs/source/tutorials#tutorials) on the general features and the **ICRA2023 Colab Tutorial** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KGqZgSYgKXF-vaYC33GR9llDsIW9Rp-q) **ICRA2024 Colab Tutorial** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JwxE7o6Z3bqCT4ewELacJ-Z1SV8xFhKK#scrollTo=QDppGIzHB9Zu)\non how to load MyoSuite models/tasks, train them, and visualize their outcome. Also, you can find [baselines](https://github.com/myohub/myosuite/tree/main/myosuite/agents) to test some pre-trained policies.\n\n\n\n## License\n\nMyoSuite is licensed under the [Apache License](LICENSE).\n\n## Citation\n\nIf you find this repository useful in your research, please consider giving a star ⭐ and cite our [arXiv paper](https://arxiv.org/abs/2205.13600)  by using the following BibTeX entrys.\n\n```BibTeX\n@Misc{MyoSuite2022,\n  author =       {Vittorio, Caggiano AND Huawei, Wang AND Guillaume, Durandau AND Massimo, Sartori AND Vikash, Kumar},\n  title =        {MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control},\n  publisher = {arXiv},\n  year = {2022},\n  howpublished = {\\url{https://github.com/myohub/myosuite}},\n  year =         {2022}\n  doi = {10.48550/ARXIV.2205.13600},\n  url = {https://arxiv.org/abs/2205.13600},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMyoHub%2Fmyosuite","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMyoHub%2Fmyosuite","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMyoHub%2Fmyosuite/lists"}