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.

翻译：在机器人学中，理解触觉交互过程中物体的形变至关重要。对形变的精确理解能够提升机器人仿真的质量，并对不同行业产生广泛影响。我们提出了一种利用物理编码图神经网络（GNNs）进行此类预测的方法。与机器人抓取和操作场景类似，我们重点模拟了在外力作用下，刚性网格与可变形网格接触时的动力学过程。我们的方法将软体与刚体均表示为图结构，其中节点承载着网格的物理状态。我们还引入了交叉注意力机制来捕捉物体间的相互作用。通过联合学习几何与物理，我们的模型能够重建一致且细节丰富的形变。我们已公开代码与数据集，以推动机器人仿真与抓取领域的研究进展。