ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation
Quick summary
arXiv:2609.16586v1 Announce Type: cross Abstract: 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 man
Key takeaways
- arXiv:2609.16586v1 Announce Type: cross Abstract: 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 man
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation” may reshape data collection, model training, output accountability and market access.
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