ManUtdMoon / softgym-reposing

SoftGym

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

SoftGym Environment for Pushing

Image	Name	Description
	PushCloth	RRT* planning for pushing cloth on the floor
	PushPants	RRT* planning for pushing pants on the floor

User Guide

Try pushing cloth with GUI:

python examples/Manual_ClothPush.py

Test RRT* planning by running:

python examples/Control_ClothPush.py

Cite

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}
}

References

• Instruction for installation of SoftGym engine: https://github.com/Xingyu-Lin/softgym