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Author

Yu Luo

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Preprint Oct 2026

PRISM: Precision Forcing for Generalist Vision-Language-Action Policies

Simulation and real-robot experiments show that PRISM preserves or improves general-task performance while raising mean end-to-end success from 38.3% to 86.7% across drive connector insertion, bimanual connector mating, and screw tightening, substantially extending the precision boundary of existing embodied policies w...

Cheng-Hao Yue, Pei-Yan Li, Hao Wang et al. · 0 citations
Preprint Sep 2026

Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning

Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command-state discrepancy may reflect control demands that motion alone does not capture. We investigate when this information matters and how to exploit it. Across three real...

Pei-Yan Li, Yue-Ran Tao, Enhao Zhang et al. · 0 citations
Open access Aug 2026

Embodied Learning under Policy and Dynamics Shifts

This work proposes Transition Occupancy Matching as a unifying principle to resolve policy and dynamics shifts within a single mathematical framework and introduces Occupancy-Matching Policy Optimization (OMPO), a novel algorithm that optimizes a surrogate objective explicitly correcting for transition discrepancies.

Yu Luo, Lei Lv, Fu-Chun Sun et al. · 0 citations

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