ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation

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 manipulation. ProxiDex reconstructs interaction point clouds and converts geometric distances into proximity cues, forming a hardware-agnostic contact representation that provides immersive feedback during VR teleoperation. Built on this representation, ProxiDex learns action-conditioned proximity dynamics with a coupled forward-inverse design: future observation latents are predicted from actions, while proximity variations are decoded from latent changes. Leveraging these dynamics, ProxiDex adaptively reweights proximity tokens across manipulation phases and uses dynamics-consistency supervision to guide policy inference, stabilizing action generation under unreliable visual feedback. Simulation and real-world experiments demonstrate improved success rates and robustness over representative baselines across standard, unseen objects, and perturbation scenarios. Additional visualizations are available at this https URL.

Accepted at the 10th Conference on Robot Learning (CoRL 2026). Project page: this https URL

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2609.16586 [cs.RO]

(or arXiv:2609.16586v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.16586 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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