Robotic manipulation of volumetric elastoplastic deformable materials, from foods such as dough to construction materials like clay, is in its infancy, largely due to the difficulty of modelling and perception in a high-dimensional space. Simulating the dynamics of such materials is computationally expensive. It tends to suffer from inaccurately estimated physics parameters of the materials and the environment, impeding high-precision manipulation. Estimating such parameters from raw point clouds captured by optical cameras suffers further from heavy occlusions. To address this challenge, this work introduces a novel Differentiable Physics-based System Identification (DPSI) framework that enables a robot arm to infer the physics parameters of elastoplastic materials and the environment using simple manipulation motions and incomplete 3D point clouds, aligning the simulation with the real world. Extensive experiments show that with only a single real-world interaction, the estimated parameters, Young's modulus, Poisson's ratio, yield stress and friction coefficients, can accurately simulate visually and physically realistic deformation behaviours induced by unseen and long-horizon manipulation motions. Additionally, the DPSI framework inherently provides physically intuitive interpretations for the parameters in contrast to black-box approaches such as deep neural networks.

本研究解决了机器人在操控体积弹塑性变形材料时所面临的建模和感知困难，尤其是在高维空间中的挑战。通过引入可微分物理基础系统辨识框架，该方法仅依靠简单的操作动作和不完整的3D点云，准确推断材料和环境的物理参数，显著提高了操控精度，并为参数提供符合物理直觉的解释。研究结果表明，单次真实世界交互即可实现对变形行为的高效模拟，为弹塑性材料的机器人操控打开了新局面。

可微分物理基础的系统辨识在机器人的弹塑性材料操控中的应用