Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact

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 …

cs.cg cs.cv cs.ro focus gnns graph graph neural networks industries interactions manipulation modeling networks neural networks physics prediction predictions robotic robotics simulations understanding