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。

物理编码图神经网络用于接触下形变预测

Physics-Encoded Graph Neural Networks for Deformation Prediction under  Contact

Reasoning over the interplay between object deformation and force
transmission through contact is central to the manipulation of compliant
objects. In this paper, we propose Neural Deforming Contact Field (NDCF), a
representation that jointly models object deformations and contact patches from
visuo-tactile feedback using implicit representations. Representing the object
geometry and contact with the environment implicitly allows a single model to
predict contact patches of varying complexity. Additionally, learning geometry
and contact simultaneously allows us to enforce physical priors, such as
ensuring contacts lie on the surface of the object. We propose a neural network
architecture to learn a NDCF, and train it using simulated data. We then
demonstrate that the learned NDCF transfers directly to the real-world without
the need for fine-tuning. We benchmark our proposed approach against a baseline
representing geometry and contact patches with point clouds. We find that NDCF
performs better on simulated data and in transfer to the real-world.

本研究提出了一种利用视触反馈隐式表示共同模拟物体变形和接触区域的神经变形接触场表示法（NDCF），并通过训练模拟数据以及无需微调便能将其应用于真实世界。实验结果表明，相对于点云表示法，NDCF 在模拟数据和真实场景下都表现更好。