Deformable Object Manipulation (DOM) is of significant importance to both daily and industrial applications. Recent successes in differentiable physics simulators allow learning algorithms to train a policy with analytic gradients through environment dynamics, which significantly facilitates the development of DOM algorithms. However, existing DOM benchmarks are either single-object-based or non-differentiable. This leaves the questions of 1) how a task-specific algorithm performs on other tasks and 2) how a differentiable-physics-based algorithm compares with the non-differentiable ones in general. In this work, we present DaXBench, a differentiable DOM benchmark with a wide object and task coverage. DaXBench includes 9 challenging high-fidelity simulated tasks, covering rope, cloth, and liquid manipulation with various difficulty levels. To better understand the performance of general algorithms on different DOM tasks, we conduct comprehensive experiments over representative DOM methods, ranging from planning to imitation learning and reinforcement learning. In addition, we provide careful empirical studies of existing decision-making algorithms based on differentiable physics, and discuss their limitations, as well as potential future directions.

翻译：可变形物体操作（Deformable Object Manipulation, DOM）在日常生活和工业应用中具有重要价值。可微物理模拟器的近期成功使学习算法能够通过环境动力学中的解析梯度来训练策略，极大促进了DOM算法的发展。然而，现有DOM基准测试要么局限于单一物体，要么缺乏可微性。这导致以下问题悬而未解：1）特定任务算法在其他任务上的泛化表现如何；2）基于可微物理的算法与非可微算法之间的通用性能对比。本文提出DaXBench——一个覆盖广泛物体类型与任务的差异化DOM基准测试平台。该平台包含9项高保真模拟挑战性任务，涵盖绳索、布料及液体操控等不同难度等级。为深入理解通用算法在不同DOM任务上的表现，我们对从规划到模仿学习及强化学习的代表性DOM方法进行了全面实验。此外，我们针对现有基于可微物理的决策算法开展了严谨的实证研究，探讨其局限性及潜在未来方向。