Merge pull request #183 from raku-slyu/main

Update on myosuite documentation page

README.md

@@ -16,15 +16,26 @@ Authors  :: Vikash Kumar ([email protected]), Vittorio Caggiano (caggiano@gma
 
 `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.
 
- [Full task details](https://github.com/myohub/myosuite/blob/main/docs/source/suite.rst#tasks) | [Baselines](https://github.com/myohub/myosuite/tree/main/myosuite/agents/baslines_NPG) | [Documentation](https://myosuite.readthedocs.io/en/latest/)
-| [Tutorials](https://github.com/myohub/myosuite/tree/main/docs/source/tutorials)
+
+ <!-- TODO: add brieft history of the mujoco environment? -->
+<!-- TODO: the tutorials section needs to format as now it is mixing all versions -->
+ <!-- [Full task details](https://github.com/myohub/myosuite/blob/main/docs/source/suite.rst#tasks) | [Baselines](https://github.com/myohub/myosuite/tree/main/myosuite/agents/baslines_NPG) | [Documentation](https://myosuite.readthedocs.io/en/latest/)
+| [Tutorials](https://github.com/myohub/myosuite/tree/main/docs/source/tutorials) -->
+
+[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">
 
 
-## Getting Started
+<!-- TODO: Section is important but seemed to me as an additional notes -->
+<!-- TODO: add tutorials and readings for RL and Gym -->
+## Before you start
+It is highly recommended to familiarize your self with the OpenAI ``gym`` (also known as [Gymnasium](https://gymnasium.farama.org/)) API. The tasks are defined through the ``env`` API and a introductory level understanding of Reinforcement Learning in general is also recommened.
+
+## 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:
@@ -43,6 +54,8 @@ Test your installation using the following command (this will return also a list
 ``` bash
 python -m myosuite.tests.test_myo
 ```
+
+<!-- TODO : include visualize images? -->
 
 You can also visualize the environments with random controls using the command below:
 ``` bash
@@ -53,10 +66,14 @@ python -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0
 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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zFuNLsrmx42vT4oV8RbnEWtkSJ1xajEo)
 
 
+ <!-- TODO: consider instructions to gym api or introduce more annotation for the code each line -->
+
 ```python
 from myosuite.utils import gym
 env = gym.make('myoElbowPose1D6MRandom-v0')
@@ -70,6 +87,14 @@ env.close()
 You can find [tutorials](https://github.com/myohub/myosuite/tree/main/docs/source/tutorials#tutorials) 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)
 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.
 
+<!-- TODO : include ICRA 2024 tutorial? -->
+
+## Projects with Myosuite
+
+TODO: Add lattice paper and other works using myosuite
+TODO: add Myodex paper
+
+
 
 ## License
 

docs/source/api.rst

@@ -0,0 +1,6 @@
+API
+==========================================
+
+.. _documentations:
+
+To be developed

docs/source/index.rst

@@ -1,19 +1,54 @@
 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
+`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>`__
+
+Before you start, we highly recommended to have a basic understanding of `OpenAI Gym API <https://gymnasium.farama.org/>`__. Principle knowledge in Reinforcement Learning is also expected for a best experience.
+
 .. note::
 
    This project is under active development.
 
+
+Main features
+============================================
+   * Bio-mechanic based simulation of musculoskeletal
+   * Task-driven controller for motion synthesis
+
+.. TODO: confirm features for myosuite
+
+
 .. toctree::
    :maxdepth: 1
    :caption: Get started
 
    install
-   suite
    tutorials
-   baselines
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Advanced Features
+
+   suite
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Documentations
+
+   api
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Projects with Myosuite
+
+   projects
+   baselines 
+
 
 .. toctree::
    :maxdepth: 1

docs/source/install.rst

@@ -67,3 +67,8 @@ For example, to use the ``myoElbowPose1D6MRandom-v0`` environment it is possible
      env.mj_render()
      env.step(env.action_space.sample()) # take a random action
    env.close()
+
+By default it will activate the simulator and the following visualization is expected:
+
+.. image:: images/test_env.png
+  :width: 300

docs/source/projects.rst

@@ -0,0 +1,107 @@
+Projects with Myosuite
+#########################################
+
+.. _projects:
+
+* :ref:`myochallenge`
+    * :ref:`myo_challenge_22`
+    * :ref:`myo_challenge_23`
+    * :ref:`myo_challenge_24`
+* :ref:`pub_with_myosuite`
+    * :ref:`ref_myodex`
+    * :ref:`ref_deprl`
+    * :ref:`ref_lattic`
+    * :ref:`ref_sar`
+
+
+.. _myochallenge:
+
+MyoChallenge
+========================================
+The Myosuite Team organised MyoChallenge to tackle difficult problems in top-level machine learning conference.
+Our latest challenge is accepted to NeuIPs 2024.
+
+.. _myo_challenge_22:
+
+MyoChallenge-2022: Learning Physiological Dexterity
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+Introducing `MyoChallenge - a NeurIPS 2022 <https://sites.google.com/view/myochallenge>`__ competition track on learning contact-rich manipulation skills for a physiologically 
+realistic musculo-skeletal hand. The goal of MyoChallenge is to push our understanding of physiological motor-control responsible
+for nimble and agile movements of the human body. In the current edition of MyoChallenge, 
+we are focusing on developing controllers for contact rich dexterous manipulation behaviors. 
+This challenge builds upon the MyoSuite ecosystem -- a fast (>4000x faster) and contact-rich framework 
+for musculoskeletal motor control. 
+
+
+
+
+Competition Tracks
+The MyoChallenge consists of two tracks:
+
+.. _myo_challenge_23:
+
+MyoChallenge-2023: Towards Human-Level Dexterity and Agility
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+Introducing `MyoChallenge 2023 <https://sites.google.com/view/myosuite/myochallenge/myochallenge-2023>`__: Towards Human-Level Dexterity and Agility
+
+Humans effortlessly grasp objects of diverse shapes and properties and execute 
+agile locomotion without overwhelming their cognitive capacities. This ability was acquired 
+through millions of years of evolution, which honed the symbiotic relationship between the central and 
+peripheral nervous systems and the musculoskeletal structure. Consequently, it is not surprising that 
+uncovering the intricacies of these complex, evolved systems underlying human movement remains a formidable 
+challenge. Advancements in neuromechanical simulations and data driven methods offer promising avenues to 
+overcome these obstacles. This year’s competition will feature two tracks: the manipulation track and the locomotion track. 
+
+.. _myo_challenge_24:
+
+Incoming Myochallenge This Year:
++++++++++++++++++++++++++++++++++++++
+About to be released
+
+
+
+
+.. _pub_with_myosuite:
+
+Publications with Myosuite
+========================================
+
+
+Please feel free to create a PR for your own project with Myosuite
+
+.. _ref_myodex:
+
+MyoDex: A Generalizable Prior for Dexterous Manipulation
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+Link avaiable at `here <https://sites.google.com/view/myodex>`__
+
+
+
+.. _ref_deprl:
+
+DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+Link avaiable at `here <https://github.com/martius-lab/depRL>`__
+
+
+
+.. _ref_lattic:
+
+Lattice: Latent Exploration for Reinforcement Learning
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+Link avaiable at `here <https://github.com/amathislab/lattice>`__
+
+
+
+.. _ref_sar:
+
+SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+Link avaiable at `here <https://sites.google.com/view/sar-rl>`__