# Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design

arXiv: https://arxiv.org/abs/2607.12105 · HTML: https://mlsanity.com/paper/2607.12105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 28 of its 65 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 28 of 28)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [In-Hand Object Rotation via Rapid Motor Adaptation](https://mlsanity.com/paper/2210.04887/) (2022)
- [Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes](https://mlsanity.com/paper/2211.11744/) (2022)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)
- [Rotating without Seeing: Towards In-hand Dexterity through Touch](https://mlsanity.com/paper/2303.10880/) (2023)
- [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023)
- [An End-to-End Differentiable Framework for Contact-Aware Robot Design](https://mlsanity.com/paper/2107.07501/) (2021)
- [Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration](https://mlsanity.com/paper/1603.06348/) (2016)
- [Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.04460/) (2020)
- [Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning](https://mlsanity.com/paper/2201.07928/) (2022)
- [AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch](https://mlsanity.com/paper/2405.07391/) (2024)
- [Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture](https://mlsanity.com/paper/2303.04705/) (2023)
- [Surprisingly Robust In-Hand Manipulation: An Empirical Study](https://mlsanity.com/paper/2201.11503/) (2022)
- [Dexterous In-Hand Manipulation of Slender Cylindrical Objects through Deep Reinforcement Learning with Tactile Sensing](https://mlsanity.com/paper/2304.05141/) (2023)
- [DROP: Dexterous Reorientation via Online Planning](https://mlsanity.com/paper/2409.14562/) (2024)
- [DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots](https://mlsanity.com/paper/2409.06613/) (2024)
- [RBO Hand 3 -- A Platform for Soft Dexterous Manipulation](https://mlsanity.com/paper/2201.10883/) (2022)
- [A Compliant, Underactuated Hand for Robust Manipulation](https://mlsanity.com/paper/1301.4394/) (2013)
- [From Simple to Complex Skills: The Case of In-Hand Object Reorientation](https://mlsanity.com/paper/2501.05439/) (2025)
- [Survey of Learning-based Approaches for Robotic In-Hand Manipulation](https://mlsanity.com/paper/2401.07915/) (2024)
- [Design and Control of Roller Grasper V2 for In-Hand Manipulation](https://mlsanity.com/paper/2004.08499/) (2020)
- [Multi-finger Manipulation via Trajectory Optimization with Differentiable Rolling and Geometric Constraints](https://mlsanity.com/paper/2408.13229/) (2024)
- [PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation](https://mlsanity.com/paper/2603.04531/) (2026)
- [ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation](https://mlsanity.com/paper/2604.11138/) (2026)

## Built on by (most cited first, 1 of 1)

- [Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration](https://mlsanity.com/paper/2610.12470/) (2026)

## Abstract (arXiv)

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 https://youtu.be/pdd1wHxQnJM?si=dM-U5kiiPTYsk3Pk.