发表机构
Faculty of Mathematics, Physics and Informatics Comenius University Bratislava（布拉迪斯拉发夸美纽斯大学数学、物理与信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究半人形机器人NICO模仿人类手臂手势的方法，基于解析几何和预训练模型，利用MediaPipe获取人体地标3D坐标计算关节角度并映射执行，实验表明该方法能从单目RGB输入产生模仿动作，也揭示了复杂姿势和手腕动作的局限。
AI 中文摘要
无缝的人机交互（HRI）要求机器人具备多种感知和运动能力，其中之一是模仿人类手势。人形机器人因其拟人特征在HRI中具有优势。本文基于解析几何和预训练的MediaPipe姿态估计模型，开发了一个用于半人形机器人NICO模仿人类手臂手势的系统。对于每个输入的RGB帧，使用MediaPipe框架获取包括手臂关节和手部关键点在内的相关人体地标3D坐标。然后根据这些坐标利用推导的几何关系计算关节角度。最后，将计算出的角度正确映射到NICO的电机配置并按预定义运动序列执行。对六名不同身高参与者的几种代表性手臂手势进行的初步实验表明，该方法仅从单目RGB输入就能产生有意义的模仿动作，同时也突出了在更复杂姿势和与手腕相关运动中的局限性。
英文摘要
Seamless human-robot interaction (HRI) requires a number of perceptual and motor abilities from the robot, one of them being the imitation of human gestures. Humanoid robots have an advantage in HRI thanks to their anthropomorphic features. In this work, we develop a system for imitation of human arm gestures by the semi-humanoid robot NICO based on analytical geometry and a pretrained MediaPipe pose-estimation model. For each input RGB frame, 3D coordinates of relevant human body landmarks, including arm joints and hand keypoints, are obtained using the MediaPipe framework. Joint angles are then computed from these coordinates using derived geometric relations. Finally, the computed angles are properly mapped to NICO's motor configuration and executed in a predefined motion sequence. Preliminary experiments on several representative arm gestures with six participants of different height indicate that the proposed method can produce meaningful imitative motions from monocular RGB input only, while also highlighting limitations in more complex poses and wrist-related movements.
Comments15 pages, 7 figures, presented at Human-Friendly Robotics workshop 2026, Trento, Italy
