# Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled Hand

arXiv: https://arxiv.org/abs/2204.03698 · HTML: https://mlsanity.com/paper/2204.03698/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2022.

## Built on by (most cited first, 20 of 20)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality](https://mlsanity.com/paper/2210.13702/) (2022)
- [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)
- [LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning](https://mlsanity.com/paper/2309.06440/) (2023)
- [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023)
- [Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation](https://mlsanity.com/paper/2309.00987/) (2023)
- [AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch](https://mlsanity.com/paper/2405.07391/) (2024)
- [Learning a Universal Human Prior for Dexterous Manipulation from Human Preference](https://mlsanity.com/paper/2304.04602/) (2023)
- [Estimator-Coupled Reinforcement Learning for Robust Purely Tactile In-Hand Manipulation](https://mlsanity.com/paper/2311.04060/) (2023)
- [Enhancing Tactile-based Reinforcement Learning for Robotic Control](https://mlsanity.com/paper/2510.21609/) (2025)
- [Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand](https://mlsanity.com/paper/2310.20350/) (2023)
- [R×R: Rapid eXploration for Reinforcement Learning via Sampling-based Reset Distributions and Imitation Pre-training](https://mlsanity.com/paper/2401.15484/) (2024)
- [Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic](https://mlsanity.com/paper/2505.13253/) (2025)
- [Learning Time-Optimal and Speed-Adjustable Tactile In-Hand Manipulation](https://mlsanity.com/paper/2411.13148/) (2024)
- [Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation](https://mlsanity.com/paper/2601.02778/) (2026)
- [On the Feasibility of A Mixed-Method Approach for Solving Long Horizon Task-Oriented Dexterous Manipulation](https://mlsanity.com/paper/2410.07403/) (2024)
- [roto 2.0: The Robot Tactile Olympiad](https://mlsanity.com/paper/2605.21429/) (2026)

## Abstract (arXiv)

We show that a purely tactile dextrous in-hand manipulation task with continuous regrasping, requiring permanent force closure, can be learned from scratch and executed robustly on a torque-controlled humanoid robotic hand. The task is rotating a cube without dropping it, but in contrast to OpenAI's seminal cube manipulation task, the palm faces downwards and no cameras but only the hand's position and torque sensing are used. Although the task seems simple, it combines for the first time all the challenges in execution as well as learning that are important for using in-hand manipulation in real-world applications. We efficiently train in a precisely modeled and identified rigid body simulation with off-policy deep reinforcement learning, significantly sped up by a domain adapted curriculum, leading to a moderate 600 CPU hours of training time. The resulting policy is robustly transferred to the real humanoid DLR Hand-II, e.g., reaching more than 46 full 2π rotations of the cube in a single run and allowing for disturbances like different cube sizes, hand orientation, or pulling a finger.