In robotics, it's crucial to understand object deformation during tactile
interactions. A precise understanding of deformation can elevate robotic
simulations and have broad implications across different industries. We
introduce a method using Physics-Encoded Graph Neural Networks (GNNs) for such
predictions. Similar to robotic grasping and manipulation scenarios, we focus
on modeling the dynamics between a rigid mesh contacting a deformable mesh
under external forces. Our approach represents both the soft body and the rigid
body within graph structures, where nodes hold the physical states of the
meshes. We also incorporate cross-attention mechanisms to capture the interplay
between the objects. By jointly learning geometry and physics, our model
reconstructs consistent and detailed deformations. We've made our code and
dataset public to advance research in robotic simulation and grasping.

机器人学中，通过物理编码的图神经网络，建模刚性网格与可变形网格间的动力学关系，以精确理解接触过程中的物体变形，推动机器人模拟与抓取研究的进展。