DAXBENCH: BENCHMARKING DEFORMABLE OBJECT MANIPULATION WITH DIFFERENTIABLE PHYSICS

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 * .

1. INTRODUCTION

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) . To facilitate fair comparison and further advancement of DOM techniques, it is important to develop a general-purpose simulation platform that accommodates these methods. We present DaXBench, a differentiable simulation framework for deformable object manipulation (DOM). In contrast with current single-task benchmarks, DaXBench encompasses a diverse range of object types, including rope, cloth, liquid, and elasto-plastic materials. The platform includes tasks with varying levels of difficulty and well-defined reward functions, such as liquid pouring, cloth folding, and rope wiping. These tasks are designed to support high-level macro actions and low-level controls, enabling comprehensive evaluation of DOM algorithms with different action spaces. We benchmark eight competitive DOM methods across different algorithmic paradigms, including sampling-based planning, reinforcement learning (RL), and imitation learning (IL). For planning methods, we consider model predictive control with the Cross Entropy Method (CEM-MPC) (Richards, 2005) , differentiable model predictive control (Hu et al., 2020) , and a combination of † These authors contributed equally. ‡ This work is partially completed at the SEA AI Lab. * The link of the project is https://github.com/AdaCompNUS/DaXBench. 1

