Dance Teaching by a Robot: Combining Cognitive and Physical Human-Robot Interaction for Supporting the Skill Learning Process
This addresses the challenge of enhancing human-robot interaction for skill acquisition in domains like dance, though it is incremental as it builds on existing teaching frameworks.
The paper tackles the problem of a robot teaching dance by combining cognitive and physical feedback to support skill learning, showing that a progressive teaching algorithm outperforms a baseline controller in initial learning and is significantly preferred by users in comfort and performance metrics at p < .01.
This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.