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AI developed at UMA_Robots demonstrates picking and scanning deformable, slippery, thin, and fragile items.
Deform360 is a large-scale visuotactile dataset with 198 objects and over 215 hours of observations for studying deformable object dynamics, enabling comparison between 2D video and 3D particle world models for robotic manipulation.
EgoPhys introduces a framework to construct deformable physical digital twins from egocentric RGB video using generalizable priors and a compact codebook, enabling zero-shot generalization to unseen objects without per-spring optimization. The system is demonstrated on a real robot, showing that egocentric human play video can serve as internal world representation for deformable-object planning.
This paper presents FreeForm, a reduced-order simulation method for deformable hyperelastic objects using a Reproducing Kernel Particle Method (RKPM) that achieves 40x faster training and lower error than neural field approaches.
PhysX-Omni is a unified framework for simulation-ready physical 3D generation covering rigid, deformable, and articulated objects, with a new dataset (PhysXVerse) and benchmark (PhysX-Bench).