Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design
This paper introduces two complementary physics priors to improve robustness in robotic in-hand rolling: a global grasp-quality prior from classical grasp analysis and a local contact-geometry prior based on fingertip curvature. Experiments show significant gains in rotation efficiency, grasp stability, and disturbance rejection, enhancing sim-to-real transfer.
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[Submitted on 13 Jul 2026]
Title:Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design
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Abstract:In-hand manipulation without external sensing is challenging due to uncertainties from finger-object contacts and disturbances by gravity. While reinforcement learning has shown promise in learning complex finger gaiting, existing approaches do not prioritize maintaining well-conditioned grasps for sustained manipulation. We introduce two complementary physics priors for robust in-hand rolling: a global grasp-quality prior derived from classical grasp analysis and a local contact-geometry prior based on fingertip curvature. The grasp-quality prior is used as a dense reward-shaping term that encourages well-distributed contacts with improved worst-case wrench resistance. The contact-geometry prior is expressed in the fingertip geometry that mechanically shapes the contact interface toward task-aligned rolling while reducing off-axis drift. We evaluate the effect of these priors on learning in-hand rolling manipulation for a multifingered robotic hand manipulating three different objects at four palm orientations. Results show significant improvement in rotation efficiency, grasp stability, and disturbance rejection, suggesting that physics priors embedded in both learning and fingertip morphology improve task robustness and sim-to-real transfer. An overview video can be found at this https URL.
Comments: 25 pages, 15 figures, 9 tables
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2607.12105 [cs.RO]
(or arXiv:2607.12105v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.12105
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shihan Lu [view email] [v1] Mon, 13 Jul 2026 19:32:41 UTC (9,926 KB)
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