DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics

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. Deformable object manipulation (DOM) is a crucial area of research with broad applications, from household (Maitin-Shepard et al., 2010; Miller et al., 2011; Ma et al., 2022) to industrial settings (Miller et al., 2012; Zhu et al., 2022). To aid in algorithm development and prototyping, several DOM benchmarks (Lin et al., 2021; Huang et al., 2021) have been developed using deformable object simulators. However, the high dimensional state and action spaces remain a significant challenge to DOM. Differentiable physics is a promising direction for developing control policies for deformable objects. It implements physical laws as differentiable computational graphs (Freeman et al., 2021; Hu et al., 2020), enabling the optimization of control policies with analytical gradients and therefore improving sample efficiency. Recent studies have shown that differentiable physics-based DOM methods can benefit greatly from this approach (Huang et al., 2021; Heiden et al., 2021; Xu et al., 2022; Chen et al., 2023).