# On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing

arXiv: https://arxiv.org/abs/2109.12720 · HTML: https://mlsanity.com/paper/2109.12720/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 26 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [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)
- [Deep Dynamics Models for Learning Dexterous Manipulation](https://mlsanity.com/paper/1909.11652/) (2019)
- [State-Only Imitation Learning for Dexterous Manipulation](https://mlsanity.com/paper/2004.04650/) (2020)
- [Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost](https://mlsanity.com/paper/1810.06045/) (2018)
- [Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.13842/) (2021)
- [Circus ANYmal: A Quadruped Learning Dexterous Manipulation with Its Limbs](https://mlsanity.com/paper/2011.08811/) (2020)
- [Geometric In-Hand Regrasp Planning: Alternating Optimization of Finger Gaits and In-Grasp Manipulation](https://mlsanity.com/paper/1804.04292/) (2018)
- [Learning Hierarchical Control for Robust In-Hand Manipulation](https://mlsanity.com/paper/1910.10985/) (2019)
- [Real-Time Robust Finger Gaits Planning under Object Shape and Dynamics Uncertainties](https://mlsanity.com/paper/1710.10350/) (2017)

## Built on by (most cited first, 11 of 11)

- [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)
- [AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch](https://mlsanity.com/paper/2405.07391/) (2024)
- [ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch](https://mlsanity.com/paper/2306.16857/) (2023)
- [Vegetable Peeling: A Case Study in Constrained Dexterous Manipulation](https://mlsanity.com/paper/2407.07884/) (2024)
- [Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllers](https://mlsanity.com/paper/2303.03533/) (2023)
- [R×R: Rapid eXploration for Reinforcement Learning via Sampling-based Reset Distributions and Imitation Pre-training](https://mlsanity.com/paper/2401.15484/) (2024)
- [On the Feasibility of A Mixed-Method Approach for Solving Long Horizon Task-Oriented Dexterous Manipulation](https://mlsanity.com/paper/2410.07403/) (2024)

## Abstract (arXiv)

Finger-gaiting manipulation is an important skill to achieve large-angle in-hand re-orientation of objects. However, achieving these gaits with arbitrary orientations of the hand is challenging due to the unstable nature of the task. In this work, we use model-free reinforcement learning (RL) to learn finger-gaiting only via precision grasps and demonstrate finger-gaiting for rotation about an axis purely using on-board proprioceptive and tactile feedback. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions that enable effective exploration of the state space. Our method can learn finger-gaiting with significantly improved sample complexity than the state-of-the-art. The policies we obtain are robust and also transfer to novel objects. Videos can be found at https://roamlab.github.io/learnfg/