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+<!-- =================================================
+# Copyright (c) Facebook, Inc. and its affiliates
+Authors  :: Vikash Kumar (vikashplus@gmail.com), Vittorio Caggiano (caggiano@gmail.com)
+================================================= -->
+<img src="https://github.com/myohub/myosuite/blob/main/docs/source/images/Full%20Color%20Horizontal%20wider.png?raw=true" width=800>
+
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+
+`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.
+
+
+
+[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)
+
+
+Below is an overview of the tasks in the MyoSuite.
+
+<img width="1240" alt="TasksALL" src="https://github.com/myohub/myosuite/blob/main/docs/source/images/myoSuite_All.png?raw=true">
+
+
+
+## Installations
+You will need Python 3.8 or later versions.
+
+It is recommended to use [Miniconda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links) and to create a separate environment with:
+``` bash
+conda create --name myosuite python=3.8
+conda activate myosuite
+```
+
+It is possible to install MyoSuite with:
+``` bash
+pip install -U myosuite
+```
+for advanced installation, see [here](https://myosuite.readthedocs.io/en/latest/install.html#alternative-installing-from-source).
+
+Test your installation using the following command (this will return also a list of all the current environments):
+``` bash
+python -m myosuite.tests.test_myo
+```
+
+
+You can also visualize the environments with random controls using the command below:
+``` bash
+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`:
+``` bash
+mjpython -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0
+```
+
+It is possible to take advantage of the latest MyoSkeleton. Once added (follow the instructions prompted by `python -m myosuite_init`), run:
+``` bash
+python -m myosuite.utils.examine_sim -s myosuite/simhive/myo_model/myoskeleton/myoskeleton.xml
+```
+
+## 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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zFuNLsrmx42vT4oV8RbnEWtkSJ1xajEo)
+
+
+
+```python
+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 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)
+on 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.
+
+
+
+## License
+
+MyoSuite is licensed under the [Apache License](LICENSE).
+
+## Citation
+
+If 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.
+
+```BibTeX
+@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},
+}
+```