Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

ArXi:2605.02699v1 Announce Type: cross 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