Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation

2026-03-30

AI Summary

Dexterous teleoperation often relies heavily on visual feedback due to inaccurate hand–robot motion mapping and the absence of tactile cues, making it difficult to perceive contact geometry and force variations. This work proposes the TAG glove system, which integrates drift-free magnetic-based hand pose tracking with 21 degrees of freedom and high-density tactile arrays—each finger equipped with 32 sensing units within a 2 cm² area—to establish a low-latency tactile closed loop. The system achieves, for the first time, millimeter-scale spatial tactile resolution and sub-degree angular accuracy, significantly improving success rates in contact-intensive teleoperation tasks and enhancing the fidelity of demonstration data.

Abstract

Teleoperation is a key approach for collecting high-quality, physically consistent demonstrations for robotic manipulation. However, teleoperation for dexterous manipulation remains constrained by: (i) inaccurate hand-robot motion mapping, which limits teleoperated dexterity, and (ii) limited tactile feedback that forces vision-dominated interaction and hinders perception of contact geometry and force variation. To address these challenges, we present TAG, a low-cost glove system that integrates precise hand motion capture with high-resolution tactile feedback, enabling effective tactile-in-the-loop dexterous teleoperation. For motion capture, TAG employs a non-contact magnetic sensing design that provides drift-free, electromagnetically robust 21-DoF joint tracking with joint angle estimation errors below 1 degree. Meanwhile, to restore tactile sensation, TAG equips each finger with a 32-actuator tactile array within a compact 2 cm^2 module, allowing operators to directly feel physical interactions at the robot end-effector through spatial activation patterns. Through real-world teleoperation experiments and user studies, we show that TAG enables reliable real-time perception of contact geometry and dynamic force, improves success rates in contact-rich teleoperation tasks, and increases the reliability of demonstration data collection for learning-based manipulation.

Problem

• dexterous teleoperation
• tactile feedback
• hand-robot motion mapping
• contact perception
• force variation

Innovation

• tactile feedback array
• dexterous teleoperation
• magnetic motion capture
• haptic glove
• contact-rich manipulation