# Dexterous In-Hand Manipulation of Slender Cylindrical Objects through Deep Reinforcement Learning with Tactile Sensing

arXiv: https://arxiv.org/abs/2304.05141 · HTML: https://mlsanity.com/paper/2304.05141/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2023.

## Built on by (most cited first, 14 of 14)

- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [Dexterous Manipulation through Imitation Learning: A Survey](https://mlsanity.com/paper/2504.03515/) (2025)
- [End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection](https://mlsanity.com/paper/2511.00139/) (2025)
- [FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.17053/) (2025)
- [Dexterous Functional Pre-Grasp Manipulation with Diffusion Policy](https://mlsanity.com/paper/2403.12421/) (2024)
- [Universal Dexterous Functional Grasping via Demonstration-Editing Reinforcement Learning](https://mlsanity.com/paper/2512.13380/) (2025)
- [Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation](https://mlsanity.com/paper/2601.02778/) (2026)
- [CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation](https://mlsanity.com/paper/2606.26423/) (2026)
- [Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design](https://mlsanity.com/paper/2607.12105/) (2026)
- [Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning](https://mlsanity.com/paper/2608.07558/) (2026)
- [Curriculum Is More Influential Than Haptic Information During Reinforcement Learning of Object Manipulation Against Gravity](https://mlsanity.com/paper/2407.09986/) (2024)
- [Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning](https://mlsanity.com/paper/2504.21585/) (2025)
- [Symmetry-Aware Fusion of Vision and Tactile Sensing via Bilateral Force Priors for Robotic Manipulation](https://mlsanity.com/paper/2602.13689/) (2026)
- [ForeTac-VLA: A Forecasting-Based Tactile-Vision-Language-Action Model for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2609.20980/) (2026)

## Abstract (arXiv)

Continuous in-hand manipulation is an important physical interaction skill, where tactile sensing provides indispensable contact information to enable dexterous manipulation of small objects. This work proposed a framework for end-to-end policy learning with tactile feedback and sim-to-real transfer, which achieved fine in-hand manipulation that controls the pose of a thin cylindrical object, such as a long stick, to track various continuous trajectories through multiple contacts of three fingertips of a dexterous robot hand with tactile sensor arrays. We estimated the central contact position between the stick and each fingertip from the high-dimensional tactile information and showed that the learned policies achieved effective manipulation performance with the processed tactile feedback. The policies were trained with deep reinforcement learning in simulation and successfully transferred to real-world experiments, using coordinated model calibration and domain randomization. We evaluated the effectiveness of tactile information via comparative studies and validated the sim-to-real performance through real-world experiments.