With the development of robot electronic skin technology, various tactile sensors, enhanced by AI, are unlocking a new dimension of perception for robots. In this work, we explore how robots equipped with electronic skin can recognize tactile gestures and interpret them as human commands. We developed a modular robot E-skin, composed of multiple irregularly shaped skin patches, which can be assembled to cover the robot's body while capturing real-time pressure and pose data from thousands of sensing points. To process this information, we propose an equivariant graph neural network-based recognizer that efficiently and accurately classifies diverse tactile gestures, including poke, grab, stroke, and double-pat. By mapping the recognized gestures to predefined robot actions, we enable intuitive human-robot interaction purely through tactile input.
@article{arxiv.2506.18256,
title = {Robot Tactile Gesture Recognition Based on Full-body Modular E-skin},
author = {Shuo Jiang and Boce Hu and Linfeng Zhao and Lawson L. S. Wong},
journal= {arXiv preprint arXiv:2506.18256},
year = {2025}
}