robohive 0.3.0

environments simulated in MuJoCo

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# Mujoco Environments mj_envs is a collection of environments/tasks simulated with the [Mujoco](http://www.mujoco.org/) physics engine and wrapped in the OpenAI gym API.

## Getting Started mj_envs uses git submodules to resolve dependencies. Please follow steps exactly as below to install correctly.

0. We recommend installaition within a conda environement. If you don’t have one yet, create one using ` conda create -n robohive python=3 `

1. Clone this repo on branch branch_name with pre-populated submodule dependencies

a. Most users - ` git clone -c submodule.mj_envs/sims/neuromuscular_sim.update=none --branch <branch_name> --recursive https://github.com/vikashplus/mj_envs.git ` b. myoSuite developers: you must have access to neuromuscular_sim(private repo) - ` git clone --branch <branch_name> --recursive https://github.com/vikashplus/mj_envs.git ` 2. Install package using pip ` $ cd mj_envs $ pip install -e . ` OR Add repo to pythonpath by updating ~/.bashrc or ~/.bash_profile ` export PYTHONPATH="<path/to/mj_envs>:$PYTHONPATH" ` 3. You can visualize the environments with random controls using the below command ` $ python mj_envs/utils/examine_env.py -e FrankaReachRandom-v0 ` FAQ: 1. 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](https://github.com/aravindr93/mjrl/tree/master/setup#known-issues) for details. 2. If FFmpeg isn’t found then run apt-get install ffmpeg on linux and brew install ffmpeg on osx (conda install FFmpeg causes some issues)

# modules mj_envs contains a variety of environements, which are organized as modules. Each module is a collection of loosely related environements. Following modules are provided at the moment with plans to improve the diversity of the collection.

## 1. Hand Manipulation Suite (HMS) HMS contains a collection of environements centered around dexterous manipulation with anthroporphic 24 degrees of freedom [Adroit Hand](https://vikashplus.github.io/P_Hand.html). These environments were designed for the publication: [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations, RSS2018](https://sites.google.com/corp/view/deeprl-dexterous-manipulation).

Hand-Manipulation-Suite Tasks [(video)](https://youtu.be/jJtBll8l_OM) :————————-: 

## 2. More coming soon