HMM-based Error Detection of Dance Step Selection for Dance Partner Robot -MS DanceR-

Abstract

We have proposed a dance partner robot, which has been developed as a platform for realizing the effective human-robot coordination with physical interactions. In the previous research, we have improved an estimation system for dance steps, which estimates the next dance step intended by a human. Although estimating the intended step is important for realizing the human-robot coordination, the cases that the estimation is failed and that the robot selects an incorrect step are not considered. Such cases have to be discussed carefully for realizing the effective human-robot coordination. In this paper, a method for error detections of dance step selections are proposed, which is designed using hidden Markov models. Experimental results illustrate the validity of the proposed method