Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural networks. In practice, this is not enough to maintain physical feasibility over long horizons and may require large amounts of interaction data to learn. We introduce PIEGraph, a novel approach to combining analytical physics and data-driven models to capture object dynamics for both rigid and deformable bodies using limited real-world interaction data. PIEGraph consists of two components: (1) a \textbf{P}hysically \textbf{I}nformed particle-based analytical model (implemented as a spring--mass system) to enforce physically feasible motion, and (2) an \textbf{E}quivariant \textbf{Graph} Neural Network with a novel action representation that exploits symmetries in particle interactions to guide the analytical model. We evaluate PIEGraph in simulation and on robot hardware for reorientation and repositioning tasks with ropes, cloth, stuffed animals and rigid objects. We show that our method enables accurate dynamics prediction and reliable downstream robotic manipulation planning, which outperforms state of the art baselines.

翻译：对于机器人操作而言，学习数据高效的物体动力学模型仍然充满挑战，尤其是针对可变形物体。一种流行的方法是将物体建模为3D粒子集合，并利用图神经网络学习其运动。然而在实践中，这不足以在长时间范围内保持物理可行性，且可能需要大量交互数据才能学习。我们提出PIEGraph，这是一种结合分析物理与数据驱动模型的新方法，能够利用有限的真实世界交互数据捕捉刚体和柔体的物体动力学。PIEGraph包含两个组成部分：（1）一个基于粒子的物理信息分析模型（实现为弹簧-质量系统），用于强制实现物理可行的运动；（2）一个等变图神经网络，采用新颖的动作表示方法，利用粒子相互作用中的对称性来引导分析模型。我们在仿真环境和机器人硬件上对绳索、布料、毛绒玩具及刚体物体的重定向与重定位任务进行了评估。结果表明，我们的方法能够实现精确的动力学预测和可靠的机器人下游操作规划，其性能优于当前最先进的基线方法。