用可组装电子皮肤+图神经网络，让机器人靠触摸识别手势指令
Robot Tactile Gesture Recognition Based on Full-body Modular E-skin
- 模块化不规则电子皮肤覆盖全身，实时采集数千个触点数据
- 基于等变图神经网络，准确识别戳、抓、摸、双拍等五类触摸手势
- 无需视觉或语音，直接通过触摸实现人机自然交互，适合服务机器人
随着机器人电子皮肤技术的发展，结合人工智能的各类触觉传感器正为机器人开辟全新的感知维度。本文探索了配备电子皮肤的机器人如何识别触觉手势并将其解读为人类指令。我们开发了一种模块化机器人电子皮肤，由多个不规则形状的皮肤贴片组成，可灵活组装以覆盖机器人全身，并实时采集来自数千个传感点的压力与姿态数据。为处理该信息，我们提出一种基于等变图神经网络的识别器，能高效且准确地分类多种触觉手势，包括戳、抓、抚摸和双击。通过将识别出的手势映射到预定义的机器人动作，我们实现了仅通过触觉输入的直观人机交互。
原文摘要 · Abstract (English)
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.
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