robohive 0.7.0
A Unified Framework for Robot Learning
License: Apache 2.0

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<!-- # RoboHive -->

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`RoboHive` is a collection of environments/tasks simulated with the [MuJoCo](http://www.mujoco.org/) physics engine exposed using the OpenAI-Gym API. Its compatible with any gym-compatible agents training framework ([Stable Baselines](https://stable-baselines3.readthedocs.io/en/master), [RLlib](https://docs.ray.io/en/latest/rllib/index.html), [TorchRL](https://pytorch.org/rl/), [AgentHive](https://sites.google.com/view/robohive/baseline), etc)

# Getting Started
   Getting started with RoboHive is as simple as -
   ``` bash
   # Install RoboHive
   pip install robohive
   # Initialize RoboHive
   robohive_init
   # Demo an environment
   python -m robohive.utils.examine_env -e FrankaReachRandom-v0
   ```

   or, alternatively for editable installation -

   ``` bash
   # Clone RoboHive
   git clone --recursive https://github.com/vikashplus/robohive.git; cd robohive
   # Install (editable) RoboHive
   pip install -e .
   # Demo an environment
   python -m robohive.utils.examine_env -e FrankaReachRandom-v0
   ```

   See [detailed installation instructions](./setup/README.md) for options on mujoco-python-bindings and  visual-encoders ([R3M](https://sites.google.com/view/robot-r3m/), [RRL](https://sites.google.com/view/abstractions4rl), [VC](https://eai-vc.github.io/)), and [frequently asked questions](https://github.com/vikashplus/robohive/wiki/6.-Tutorials-&-FAQs#installation) for more details.

# Suites
*RoboHive* contains a variety of environments, which are organized as suites. Each suite is a collection of loosely related environments. The following suites are provided at the moment with plans to improve the diversity of the collection.

**Hand-Manipulation-Suite** [(video)](https://youtu.be/jJtBll8l_OM)
:-------------------------:
![Alt text](https://raw.githubusercontent.com/vikashplus/robohive/f786982204e85b79bd921aa54ffebf3a7887de3d/mj_envs/hand_manipulation_suite/assets/tasks.jpg?raw=false "Hand Manipulation Suite") A collection of environments centered around dexterous manipulation. Standard ADROIT benchmarks introduced in [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations, RSS2018](https://sites.google.com/corp/view/deeprl-dexterous-manipulation).) are a part of this suite


Arm-Manipulation-Suite
:-------------------------:
![Alt text](https://github.com/vikashplus/robohive/assets/12837145/ef072b90-42e7-414b-9da0-45c87c31443a?raw=false "Arm Manipulation Suite") A collection of environments centered around Arm manipulation.


Myo-Suite [(website)](https://sites.google.com/view/myosuite)
:-------------------------:
![Alt text](https://github.com/vikashplus/robohive/assets/12837145/0db70854-cb90-4360-8bd9-42cd1b5446c1?raw=false "Myo_Suite") A collection of environments centered around Musculoskeletal control.


Myo/MyoDM-Suite [(Website)](https://sites.google.com/view/myodex)
:-------------------------:
![myodm_task_suite](https://github.com/vikashplus/robohive/assets/12837145/2ca62e77-6827-4029-930e-b95ab86ae0f4) A collection of musculoskeletal environments for dexterous manipulation introduced as MyoDM in [MyoDeX](https://sites.google.com/view/myodex).


MultiTask Suite
:-------------------------:
![Alt text](https://github.com/vikashplus/robohive/assets/12837145/b7f314b9-8d4e-4e58-b791-6df774b91d21?raw=false "Myo_Suite") A collection of environments centered around multi-task. Standard [RelayKitchen benchmarks](https://relay-policy-learning.github.io/) are a part of this suite.

## - TCDM Suite (WIP)
   This suite contains a collection of environments centered around dexterous manipulation. Standard [TCDM benchmarks](https://pregrasps.github.io/) are a part of this suite

## - ROBEL Suite (Coming soon)
   This suite contains a collection of environments centered around real-world locomotion and manipulation. Standard [ROBEL benchmarks](http://roboticsbenchmarks.org/) are a part of this suite

# Citation
If you find `RoboHive` useful in your research,
- please consider supporting the project by providing a [star ⭐](https://github.com/vikashplus/robohive/stargazers)
- please consider citing our project by using the following BibTeX entry:



```bibtex
@Misc{RoboHive2020,
  title = {RoboHive -- A Unified Framework for Robot Learning},
  howpublished = {\url{https://sites.google.com/view/robohive}},
  year = {2020},
  url = {https://sites.google.com/view/robohive},
}