This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs.

本研究针对人机交互中的一个关键问题，提出了一种新颖的方法，使Nao机器人能够实时模仿人类的头部动作、眨眼和情感表达。利用MediaPipe和DeepFace的强大能力，研究展示了如何通过闭环反馈系统提高头部动作的精确性，尤其是对自闭症儿童的沟通改善具有重要意义。

实时模仿人类头部动作、眨眼和情感的Nao机器人：闭环方法