DaxBench: Benchmarking Deformable Object Manipulation with Differentiable Physics
Siwei Chen, Yiqing Xu, Cunjun Yu, Linfeng Li, Xiao Ma, Zhongwen Xu, David Hsu
Abstract
Deformable object manipulation (DOM) is a long-standing challenge in robotics and has attracted significant interest recently. This paper presents DaXBench, a differentiable simulation framework for DOM. While existing work often focuses on a specific type of deformable objects, DaXBench supports fluid, rope, cloth ...; it provides a general-purpose benchmark to evaluate widely different DOM methods, including planning, imitation learning, and reinforcement learning. DaXBench combines recent advances in deformable object simulation with JAX, a high-performance computational framework. All DOM tasks in DaXBench are wrapped with the OpenAI Gym API for easy integration with DOM algorithms. We hope that DaXBench provides to the research community a comprehensive, standardized benchmark and a valuable tool to support the development and evaluation of new DOM methods. The code and video are available online.
BibTeX
@inproceedings{
chen2023daxbench,
title={DaxBench: Benchmarking Deformable Object Manipulation with Differentiable Physics},
author={Siwei Chen and Yiqing Xu and Cunjun Yu and Linfeng Li and Xiao Ma and Zhongwen Xu and David Hsu},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=1NAzMofMnWl}
}