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Zi-Wei Wang

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Nov 2026

Efficient Online Robotic Reinforcement Learning With Explicit Manifold Alignment

Online reinforcement learning (RL) enables robots to continuously improve through real-world interaction, yet achieving high success rates in contact-rich manipulation remains challenging due to sparse binary rewards. Conventional reward-shaping methods rely on hand-designed goal-proximity heuristics and seldom differe...

Wen Guo, Pei-Zhi Tang, Yu-Kun Bai et al. · 0 citations
Preprint Aug 2026

AffordTrajDP: Dynamic Affordance-Guided Visuomotor Policy Learning for Robotic Manipulation

Affordance-guided imitation learning has shown impressive performance in robotic manipulation tasks by compressing visual perception into task-specific geometric constraints (e.g., fixed contact points). However, the commonly used static affordances can become inconsistent in precision-critical tasks or under object lo...

Gao-Yuan Wu, Ziyu Shan, Hao-Yang Du et al. · 0 citations
#machine learning Preprint Sep 2026

Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation

Facet-0 unifies multimodal representation learning and reinforcement learning post-training around a joint action-wrench proposal, which reaches 82% mean success on five sub-millimeter computer-assembly tasks, compared with 15% for the strongest baseline.

Hao-Yuan Deng, Hai-Chao Liu, Wen-Kai Guo et al. · 1 citation

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