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

2025-09-18

AI Summary

This study addresses the tactile sensor placement optimization problem for dexterous in-hand manipulation with anthropomorphic robotic hands, investigating how multi-region tactile feedback—beyond the fingertips (e.g., volar pads, palm)—affects robustness and precision in object reorientation tasks. We propose a geometry- and dynamics-aware tactile sensor distribution optimization method and develop a deep reinforcement learning control framework that integrates distributed tactile inputs. Performance of diverse sensing configurations is systematically evaluated in both simulation and physical experiments. Results demonstrate that non-tip regions provide critical information for stable grasping and fine-grained reorientation. Our adaptive placement strategy significantly improves task success rate (+23.6%) and orientation accuracy (31.4% reduction in angular error), validating the efficacy of coordinated multi-region tactile perception. This work establishes a quantitative, task-driven paradigm for tactile layout optimization in anthropomorphic hands.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• Determining optimal tactile sensor placement in robotic hands
• Assessing impact of non-fingertip tactile feedback on manipulation
• Analyzing relationship between object properties and sensor configuration

Innovation

• Optimizing tactile sensor distribution on robotic hands
• Analyzing tactile feedback from fingers and palm
• Enhancing deep reinforcement learning control policies