# The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation

arXiv: https://arxiv.org/abs/2509.14984 · HTML: https://mlsanity.com/paper/2509.14984/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 23 of 23)

- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [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)
- [Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research](https://mlsanity.com/paper/1802.09464/) (2018)
- [Rotating without Seeing: Towards In-hand Dexterity through Touch](https://mlsanity.com/paper/2303.10880/) (2023)
- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry](https://mlsanity.com/paper/2104.01167/) (2021)
- [TriFinger: An Open-Source Robot for Learning Dexterity](https://mlsanity.com/paper/2008.03596/) (2020)
- [TacSL: A Library for Visuotactile Sensor Simulation and Learning](https://mlsanity.com/paper/2408.06506/) (2024)
- [Digitizing Touch with an Artificial Multimodal Fingertip](https://mlsanity.com/paper/2411.02479/) (2024)
- [Learning to Grasp Without Seeing](https://mlsanity.com/paper/1805.04201/) (2018)
- [Surprisingly Robust In-Hand Manipulation: An Empirical Study](https://mlsanity.com/paper/2201.11503/) (2022)
- [FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors](https://mlsanity.com/paper/2410.02048/) (2024)
- [In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation](https://mlsanity.com/paper/2002.12160/) (2020)
- [Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation](https://mlsanity.com/paper/2102.11051/) (2021)
- [RBO Hand 3 -- A Platform for Soft Dexterous Manipulation](https://mlsanity.com/paper/2201.10883/) (2022)
- [Learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects](https://mlsanity.com/paper/2407.18834/) (2024)
- [Survey of Learning-based Approaches for Robotic In-Hand Manipulation](https://mlsanity.com/paper/2401.07915/) (2024)
- [Improving Grasp Stability with Rotation Measurement from Tactile Sensing](https://mlsanity.com/paper/2108.00301/) (2021)
- [Sensorized Soft Skin for Dexterous Robotic Hands](https://mlsanity.com/paper/2404.19448/) (2024)
- [Force Feedback Control For Dexterous Robotic Hands Using Conditional Postural Synergies](https://mlsanity.com/paper/2303.06170/) (2023)
- [Data-Driven Optimization of Tactile Sensor Configurations for Efficient Dexterous Manipulation](https://mlsanity.com/paper/2409.20473/) (2024)

## Abstract (arXiv)

In-hand manipulation tasks, particularly in human-inspired robotic systems, must rely on distributed tactile sensing to achieve precise control across a wide variety of tasks. However, the optimal configuration of this network of sensors is a complex problem, and while the fingertips are a common choice for placing sensors, the contribution of tactile information from other regions of the hand is often overlooked. This work investigates the impact of tactile feedback from various regions of the fingers and palm in performing in-hand object reorientation tasks. We analyze how sensory feedback from different parts of the hand influences the robustness of deep reinforcement learning control policies and investigate the relationship between object characteristics and optimal sensor placement. We identify which tactile sensing configurations contribute to improving the efficiency and accuracy of manipulation. Our results provide valuable insights for the design and use of anthropomorphic end-effectors with enhanced manipulation capabilities.