Human preference learning by robot partners based on human localization

This paper discusses the human preference learning by robot partners through interaction with a person and human position based on sensor network. We use a robot music player; Miuro, and we focus on the music selection for providing the comfortable sound field for the person. We propose a learning method of the relationship between human position and its corresponding music selection based on Q-learning. Furthermore, we propose a steady-state genetic algorithm using template matching to extract a person in 3D distance image based on differential extraction. The experimental results show that the proposed method can learn the relationship between human position and its corresponding human preferable music.