用可编织触觉皮肤实现手势控制机器人，让操作更自然高效。
Towards Intuitive Human-Robot Interaction through Embodied Gesture-Driven Control with Woven Tactile Skins
- 用导电纱线编织的柔性触觉皮肤，可贴合曲面并感知多点触控。
- 14种手势识别准确率接近100%，任务完成时间比传统方式快57%。
- 适合需要直观人机交互的工业场景，如机械臂抓取与倒液操作。
本文提出一种新型人-机器人交互框架，通过基于电容的可编织触觉皮肤实现手势驱动控制。与依赖面板或手持设备的传统接口不同，该触觉皮肤可无缝集成于机器人曲面，实现具身化交互，缩小人类意图与机器人响应之间的差距。其编织结构结合了织物般的柔韧性和结构稳定性，并通过交织的导电纱线实现密集的多通道传感。在此基础上，我们定义了涵盖典型机器人指令的14种单点与多点触控手势映射，包括任务空间运动和辅助功能。设计了一种轻量级卷积-变压器模型，实现实时手势识别，准确率接近100%，优于先前基线方法。在机械臂任务（包括抓取-放置和倾倒）实验中，系统将任务完成时间最多缩短57%，相比键盘面板和示教器。整体而言，本框架为更自然、高效的具身化人机交互提供了可行路径。
原文摘要 · Abstract (English)
This paper presents a novel human-robot interaction (HRI) framework that enables intuitive gesture-driven control through a capacitance-based woven tactile skin. Unlike conventional interfaces that rely on panels or handheld devices, the woven tactile skin integrates seamlessly with curved robot surfaces, enabling embodied interaction and narrowing the gap between human intent and robot response. Its woven design combines fabric-like flexibility with structural stability and dense multi-channel sensing through the interlaced conductive threads. Building on this capability, we define a gesture-action mapping of 14 single- and multi-touch gestures that cover representative robot commands, including task-space motion and auxiliary functions. A lightweight convolution-transformer model designed for gesture recognition in real time achieves an accuracy of near-100%, outperforming prior baseline approaches. Experiments on robot arm tasks, including pick-and-place and pouring, demonstrate that our system reduces task completion time by up to 57% compared with keyboard panels and teach pendants. Overall, our proposed framework demonstrates a practical pathway toward more natural and efficient embodied HRI.
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