Deformable object manipulation is a long-standing challenge in robotics. While existing approaches often focus narrowly on a specific type of object, we seek a general-purpose algorithm, capable of manipulating many different types of objects: beans, rope, cloth, liquid, . . . . One key difficulty is a suitable representation, rich enough to capture object shape, dynamics for manipulation and yet simple enough to be acquired effectively from sensor data. Specifically, we propose Differentiable Particles (DiPac), a new algorithm for deformable object manipulation. DiPac represents a deformable object as a set of particles and uses a differentiable particle dynamics simulator to reason about robot manipulation. To find the best manipulation action, DiPac combines learning, planning, and trajectory optimization through differentiable trajectory tree optimization. Differentiable dynamics provides significant benefits and enable DiPac to (i) estimate the dynamics parameters efficiently, thereby narrowing the sim-to-real gap, and (ii) choose the best action by backpropagating the gradient along sampled trajectories. Both simulation and real-robot experiments show promising results. DiPac handles a variety of object types. By combining planning and learning, DiPac outperforms both pure model-based planning methods and pure data-driven learning methods. In addition, DiPac is robust and adapts to changes in dynamics, thereby enabling the transfer of an expert policy from one object to another with different physical properties, e.g., from a rigid rod to a deformable rope.

翻译：可变形物体操作是机器人领域长期存在的挑战。现有方法通常局限于特定类型的物体，而本文旨在提出一种通用算法，能够操作多种不同类型的物体：豆类、绳索、布料、液体等。关键难点在于寻找合适的表征——既要足够丰富以捕捉物体形状和操作动力学，又要足够简洁以便从传感器数据中有效获取。为此，我们提出可微粒子（DiPac）——一种用于可变形物体操作的新算法。DiPac将可变形物体表示为粒子集合，并利用可微粒子动力学模拟器进行机器人操作推理。为寻找最优操作动作，DiPac通过可微轨迹树优化将学习、规划与轨迹优化相结合。可微动力学带来显著优势，使DiPac能够：（i）高效估计动力学参数，从而缩小仿真到现实的差距；（ii）通过沿采样轨迹反向传播梯度选择最优动作。仿真与真实机器人实验均展现出令人鼓舞的结果。DiPac可处理多种物体类型，并通过规划与学习的结合，在性能上优于纯模型驱动规划方法和纯数据驱动学习方法。此外，DiPac具有鲁棒性，能适应动力学变化，从而可将专家策略从一种物体迁移至物理性质不同的另一物体（例如从刚性杆迁移至可变形绳索）。