  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.
