Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions
Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural networks. In practice, this is not enough to maintain physical feasibility over long horizons and may require large amounts of interaction data to learn. We introduce PIEGraph, a novel approach to combining analytical physics and data-driven models to capture object dynamics for both rigid and deformable bodies using limited real-world interaction data. PIEGraph consists of two components: (1) a \textbf{P}hysically \textbf{I}nformed particle-based analytical m...
| Authors | Sergio Orozco, Tushar Kusnur, Brandon May et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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