DECISION MAKING OF ROBOT PARTNERS BASED ON FUZZY CONTROL AND BOLTZMANN SELECTION

Abstract

This paper discusses the social learning of robot partners through interaction with a person. We use a robot music player; Miuro, and we focus on the music selection for providing the comfortable sound field for the person. First, we propose the control architecture of Miuro based on autonomous behavior mode, interactive behavior mode, and human control mode. Next, we propose a learning method of the relationship between human interaction and its corresponding reaction based on Boltzmann selection, adaptive reward function, and temperature control. The experimental results show that the proposed method can learn the relationship between human interaction and its corresponding behavior, even if the human intention is changed in the learning. Furthermore, the experimental results show that the proposed method can provide the person the preferable song as the comfortable sound field.