Abstract
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.
Abstract (translated)
随着机器人电子皮肤技术的发展，各种由人工智能增强的触觉传感器为机器人开启了感知的新维度。在这项工作中，我们探索了配备有电子皮肤的机器人如何识别触觉手势并将其解释为人机命令。我们开发了一种模块化的机器人E-皮肤，它由多个不规则形状的皮肤补丁组成，可以组装覆盖机器人的身体，并从数千个感测点实时捕捉压力和姿态数据。为了处理这些信息，我们提出了一种基于等变图神经网络的识别器，能够高效且准确地分类包括戳、抓取、抚摸和双拍在内的各种触觉手势。通过将识别出的手势映射到预定义的机器人动作上，我们实现了仅通过触觉输入进行直观的人机交互。
URL
https://arxiv.org/abs/2506.18256
