[NatureMachineIntelligence25] Embedding high-resolution touch across robotic hands enables adaptive human-like grasping
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.
Zihang Zhao
Ph.D. '22
My research interests include robotics, mechatronics, and tactility-related robot cognition, etc.
Yuyang Li
Ph.D. '24
My research interests lie in the intersection of 3D computer vision, computer graphics, and robotics. My long-term goal is to create intelligence that perceives, understands, and interacts with the physical / virtual environments.
Tengyu Liu
Research Scientist
Hangxin Liu
Research Scientist
