DaxBench

Deformable object manipulation (DOM) is a long-standing challenge in robotics and has attracted significant interest recently. This work 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.

Documentation

Getting Started

Support

If you are having issues, please let us know by filing an issue on our issue tracker.

License

DaxBench is licensed under the Apache 2.0 License.

Citing

If you find DaxBench useful, please cite it in your publications.

@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},
    year={2023},
    booktitle={ICLR}
}