Vision-Tactile-Language-Action (VTLA) models have demonstrated clear advantages over Vision-Language-Action (VLA) models in contact-rich manipulation. However, developing VTLA models is severely constrained by the massive amounts of vision-tactile data and computational resources required. To address this bottleneck, w...
Yan-Song Wu, Tuo-Cheng Yang, Rong-Ping Zhao et al.· 0 citations
Force-aware Vision-Language-Action (VLA) policies improve contact-rich manipulation, but typically combine task-level motion and contact-dependent adjustment in a single action prediction. Demonstrations provide no explicit labels for decomposing that prediction into a reusable reference action and a correction. We pre...
Ju Dong, Yuan-You Fu, Jian Chen et al.· 0 citations
This work presents a gated residual coordination framework that keeps both modules frozen and applies bounded corrections to their outputs, and describes the contributions of reward gating, adaptive correction authority, and separate body and hand correction heads.
Rui Wu, Shuang Li, Li-Ding Zhang et al.· 1 citation
ThorArena is presented, a benchmark for evaluating force-aware humanoid interaction based on human demonstrations with synchronized motion and force measurements and a unified benchmark protocol that replays recorded interaction forces in simulation and provides a standardized evaluation interface for different humanoi...
D-SafeMPC improves safety, task success rates, and planning efficiency over state-of-the-art baselines, and employs an iterative-projection scheme where an MPC refines the trajectory at each denoising step.
Erdi Sayar, Ersin Daş, Joel W. Burdick et al.· arXiv.org· 0 citations
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