Embedding high-resolution touch across robotic hands enables adaptive human-like grasping

2024-12-19

arXiv.org

AI Summary

Robotic hands struggle with human-like adaptive manipulation in dynamic environments due to insufficient tactile feedback. Method: We propose F-TAC, a biomimetic robotic hand that—while preserving full joint degrees of freedom—integrates a high-density, flexible tactile sensor array covering 70% of the hand surface with 0.1 mm spatial resolution. Combining biomechanics-informed structural optimization and generative hand-configuration synthesis, we establish an embodied tactile closed-loop control framework. Results: In 600 real-world dynamic grasping trials, F-TAC significantly outperforms a non-tactile baseline (p < 0.0001), demonstrating robust adaptive adjustment. This work reveals the critical role of high-fidelity embodied tactile sensing in enhancing robotic intelligent behavior and establishes a scalable tactile-motor co-design paradigm for embodied intelligence.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Developing robotic hands that adapt to real-world dynamics remains a fundamental challenge in robotics and machine intelligence. Despite significant advances in replicating human hand kinematics and control algorithms, robotic systems still struggle to match human capabilities in dynamic environments, primarily due to inadequate tactile feedback. To bridge this gap, we present F-TAC Hand, a biomimetic hand featuring high-resolution tactile sensing (0.1mm spatial resolution) across 70% of its surface area. Through optimized hand design, we overcome traditional challenges in integrating high-resolution tactile sensors while preserving the full range of motion. The hand, powered by our generative algorithm that synthesizes human-like hand configurations, demonstrates robust grasping capabilities in dynamic real-world conditions. Extensive evaluation across 600 real-world trials demonstrates that this tactile-embodied system significantly outperforms non-tactile-informed alternatives in complex manipulation tasks (p<0.0001). These results provide empirical evidence for the critical role of rich tactile embodiment in developing advanced robotic intelligence, offering new perspectives on the relationship between physical sensing capabilities and intelligent behavior.

Problem

• Develop robotic hands with human-like adaptive grasping
• Integrate high-resolution tactile sensing in robotic hands
• Enhance robotic intelligence through tactile feedback

Innovation

• High-resolution tactile sensing on robotic hands
• Generative algorithm for human-like grasping
• Optimized design preserves full motion range