Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation

TL;DR

This paper introduces TAG, a low-cost tactile glove system that combines precise hand motion capture with high-resolution tactile feedback to enhance dexterous teleoperation and robotic manipulation.

Contribution

The paper presents a novel tactile glove integrating magnetic sensing for accurate motion tracking and a tactile array for contact perception, improving teleoperation performance.

Findings

TAG enables real-time perception of contact geometry and force.

The system improves success rates in contact-rich teleoperation tasks.

User studies show increased reliability in demonstration data collection.

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.