A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilities that later tasks can reuse. Robot agents that act through code can already repair programs from execution feedback, yet it remains a central challenge to organize this...
Zi-Mo Wen, Yi-Jin Chen, Yuxuan Cao et al.· 4 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
Across towel folding, book insertion, and Hanoi ring placement, LIFT learns faster and reaches higher performance than vision-only post-training, while ablations show that reactive force memory and online corrective data are both important for robust contact-rich manipulation.
Yi Wang, Wen-Di Chen, Zi-Mo Wen et al.· arXiv.org· 4 citations
A system level acceleration strategy that reduces computation in both perception and action generation and compress diffusion sampling into a compact 2-step schedule through efficiency oriented training while preserving action precision is proposed.
The Triplet-to-Track System (TTS), a closed-loop long-horizon imitation learning system that uses human videos to reduce reliance on robot-collected data, achieves a 74.8\% average success rate and supports object-level and compositional generalization.
Jianxiang Liu, Gao-Jing Zhang, Chuan Wen et al.· 0 citations
Results show that a fixed-weight, self-evolving harness can revise, recover, and accumulate verified approaches while producing structured trajectories for future supervised and reinforcement learning.
Boxiu Li, Zi-Mo Wen, Yi-Jia Fan et al.· 2 citations· ⚡1
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