ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation
Multi-finger dexterous manipulation relies on stable hand-object interactions, yet these interactions are partially observable in practice. Visual observations are often occluded by the hand, tactile sensors introduce hardware-specific modalities and calibration burdens, and existing policies rarely model how these cues evolve under actions, making them brittle under contact uncertainty. To address these, we present ProxiDex, a dynamics-guided proximity policy framework that treats hand-object proximity as an interaction state for dexterous manipulation. ProxiDex reconstructs interaction point clouds and converts geometric distances into proximity cues, forming a hardware-agnostic contact...
| Authors | Yushan Bai, Boyu Zheng, Zhiyang Mao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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