Embedding high-resolution touch across robotic hands enables adaptive human-like grasping
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Dec 2024.
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Citing papers per year: 2025: 2; 2026: 6.
8 papers.
- 2025Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulationcited by 6
- 2025Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulationcited by 6
- 2026TacCoRL: Integrating Tactile Feedback into VLA via Simulationcited by 3
- 2026Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learningcited by 3
- 2026Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesiscited by 1
- 2026Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactionscited by 0
- 2026TrackDeform3D: Markerless and Autonomous 3D Keypoint Tracking and Dataset Collection for Deformable Objectscited by 0
- 2026Let Robots Feel Your Touch: Visuo-Tactile Cortical Alignment for Embodied Mirror Resonancecited by 0
- 2026Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis3 Sep 2026
- 2026Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning10 Jun 2026
- 2026TacCoRL: Integrating Tactile Feedback into VLA via Simulation10 Jun 2026
- 2026Let Robots Feel Your Touch: Visuo-Tactile Cortical Alignment for Embodied Mirror Resonance14 May 2026
- 2026TrackDeform3D: Markerless and Autonomous 3D Keypoint Tracking and Dataset Collection for Deformable Objects17 Mar 2026
- 2026Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions7 Feb 2026
- 2025Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation10 Dec 2025
- 2025Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation17 Apr 2025