Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911771348959232 |
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| author | Saleh, Mahdi Sommersperger, Michael Navab, Nassir Tombari, Federico |
| author_facet | Saleh, Mahdi Sommersperger, Michael Navab, Nassir Tombari, Federico |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact Saleh, Mahdi Sommersperger, Michael Navab, Nassir Tombari, Federico Computer Vision and Pattern Recognition Computational Geometry Robotics 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. |
| title | Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact |
| topic | Computer Vision and Pattern Recognition Computational Geometry Robotics |
| url | https://arxiv.org/abs/2402.03466 |