Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retargeted motions to fail at producing the intended object behavior. We present \textbf{FoLD}, a framework for learning dexterous manipulation of articulated objects through...
Hao-Wei Shen, Ti-Ngai Li, Yu-Meng Liu et al.· 0 citations
Grasp synthesis aims to generate stable and physically plausible hand--object interactions, and has become a fundamental problem in both human hand modeling and robotic manipulation. However, a unified representation across human and robotic hands is still lacking, mainly due to differences in hand morphology and surfa...
Xuan-Ze Yang, Yu-Meng Liu, Hai-Yang Xin et al.· 0 citations
This work introduces a novel motion prior based on the sparsity of high‐order temporal derivatives, serving as a kinematic proxy for impulsive force generation and achieves linear complexity, enabling efficient processing of long sequences.