Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design
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-geomet...
| Authors | Yifei Chen, Shihan Lu, Ed Colgate et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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