Assembling Two Parts in One Hand

Abstract:A hallmark of human dexterity is the cooperative use of fingers, where different fingers take on distinct yet coordinated roles to accomplish fine manipu- lation, such as capping a pen with the hand that holds it. We study this finger-level coordination through in-hand assembly: mating two rigid objects within a single dexterous hand, with no second arm and no fixture. We present a reinforcement learning formulation to solve this problem in a unified framework, which is driven by a goal relative pose between the two parts. Finger coordination is shaped by a function-based auxiliary reward and regularized toward a single human reference pose, while domain randomization and a fusion of historical proprioception and object observation confer robustness to occlusion-induced estimation noise. The same recipe solves three different assembly tasks (Bottle, Syringe, and Marker). Trained purely in simulation, the policies transfer zero-shot to hardware with a single camera, demonstrating robustness to state-estimation errors caused by oc- clusion. Our experiments also reveal that in-hand assembly places demands on hand morphology and can serve as a benchmark for modern robotic hand systems. Videos and code are available at this https URL.

To appear on Conference on Robot Learning (CoRL) 2026. Project website: this https URL

Robotics (cs.RO)

arXiv:2609.10137 [cs.RO]

(or arXiv:2609.10137v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.10137 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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