SoftGym is a set of benchmark environments for deformable object manipulation including tasks involving fluid, cloth and rope. It is built on top of the Nvidia FleX simulator and has standard Gym API for interaction with RL agents. A number of RL algorithms benchmarked on SoftGym can be found in SoftAgent
Image | Name | Description |
---|---|---|
PushCloth | RRT* planning for pushing cloth on the floor | |
PushPants | RRT* planning for pushing pants on the floor |
Try pushing cloth with GUI:
python examples/Manual_ClothPush.py
Test RRT* planning by running:
python examples/Control_ClothPush.py
If you find this codebase useful in your research, please consider citing:
@inproceedings{corl2020softgym,
title={SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation},
author={Lin, Xingyu and Wang, Yufei and Olkin, Jake and Held, David},
booktitle={Conference on Robot Learning},
year={2020}
}
- Instruction for installation of SoftGym engine: https://github.com/Xingyu-Lin/softgym