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该论文提出一种物理注意力偏置（physical attention bias），在注意力logits上加入基于弧长距离和欧氏距离的可学习项，以改进对未训练过的电缆等可变形线性对象（DLOs）的运动预测，显著减少长期rollout中的漂移与误差。
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).