Soft-robotics control environment package for OpenAI Gym
Project description
Soft-robot Control Environment (gym-softrobot)
The environment is designed to leverage reinforcement learning methods into soft-robotics control, inspired from slender-body living creatures. The code is built on PyElastica, an open-source physics simulation for slender structure. We intend this package to be easy-to-install and fully compatible to OpenAI Gym.
Requirements:
- Python 3.8+
- OpenAI Gym
- PyElastica 0.2+
- Matplotlib (optional for display rendering and plotting)
Please use this bibtex to cite in your publications:
@misc{gym_softrobot,
author = {Chia-Hsien Shih, Seung Hyun Kim, Mattia Gazzola},
title = {Soft Robotics Environment for OpenAI Gym},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/skim0119/gym-softrobot}},
}
Installation
pip install gym-softrobot
Reinforcement Learning Example
We tested the environment using Stable Baselines3 for centralized control. More advanced algorithms are still under development.
Environment Design
Included Environments
Octopus[Multi-arm control]
octo-flat
[2D]octo-reach
octo-swim
octo-flat
Contribution
We are currently developing the package internally.
Project details
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