Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction. This raises a fundamental question: can robots learn deployable visual-tactile dexterous manipulation policies from human video demonstrations without robot-side data...
Ruo-Qu Chen, Fei-Xiang Ruan, Liu Cao et al.· 5 citations
Dexterous in-hand manipulation requires policies that coordinate high-DoF hand joints through intermittent, contact-rich interaction. Beyond target-orientation tracking, such policies must discover finger gaits that preserve object stability while adapting to geometry, anisotropy, pose, contact, and sensing changes. We...
Jie Yin, Wan-Li Xing, Ze-Yuan Zhao et al.· 0 citations
A Unified Visual-Tactile-Action Model is proposed that maps both embodiments into aligned tactile and action representations and jointly predicts future action and tactile trajectories and achieves an average success rate of 70%, outperforming the strongest visual-tactile baseline, which achieves 29%, and an architectu...
Wen-Qiao Li, Qian-You Zhao, Jia-Wen Hao et al.· 0 citations
It is shown that a flexible and expressive transformer, PointZero, outperforms prior methods on the same data and demonstrates the utility of the pre-training objective by post-training PointZero for two downstream applications: action-conditioned 3D dynamics prediction and imitation learning.
B. Duisterhof, Kai-Feng Zhang, Adam Hung et al.· 3 citations· ⚡1
POISE (Palm-relative Object reaching In SE(3), a sim-to-real reinforcement learning framework for in-hand 6D object pose reaching, combines diverse stable-grasp initialization, goal- and geometry-conditioned control, an adaptive 6D goal curriculum, and a compact reward scheme for pose reaching and grasp preservation.
Jun-Xiao Lin, Tian-Yue Wu, Jie Yin et al.· 0 citations
Experiments in both simulation and real-world settings demonstrate that R2S-Eval produces reliable and stable policy conclusions, achieves agreement with human preferences, substantially reduces repeated hardware-operation effort, and reveals behavior-quality differences that are not captured by binary success labels.
Yi-Di Wang, Fei-Xiang Ruan, Ruo-Qu Chen et al.· 2 citations
Agentic Real2Sim is introduced, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states.
