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+Welcome to MyoSuite's documentation!
+=====================================
+
+`MyoSuite <https://sites.google.com/view/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.
+
+Check our `github repository <https://github.com/MyoHub/myosuite>`__ for more technical details.
+
+Our paper can be found at: `https://arxiv.org/abs/2205.13600 <https://arxiv.org/abs/2205.13600>`__
+
+Advanced user are invited to familiarize themselves with the basics of the `OpenAI Gym API <https://gymnasium.farama.org/>`__ and review the basic principle of Reinforcement Learning to make the most out of MyoSuite features and functionalities
+
+.. note::
+
+   This project is under active development.
+
+
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Get started
+
+   install
+   tutorials
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Advanced Features
+
+   suite
+   
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Projects with Myosuite
+
+   projects
+   baselines
+   challenge-doc
+
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: References
+
+   publications
+
+
+How to cite
+-----------
+
+.. code-block:: bibtex
+
+   @article{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},
+   }