Human-adaptive step estimation method for a dance partner robot

Abstract

A ballroom dance is a performance between a male dancer and a female dancer and consists of its own steps. The dance is led by a male dancer and a female dancer continues to dance by estimating the following step through physical interaction between them. A dance partner robot, PBDR, has been proposed as a research platform for human-robot coordination. It dances a waltz with a male dancer by estimating the following step led by the male dancer. The step estimator has been designed based on the hidden Markov model. The parameters of the hidden Markov model are determined based on a set of time series data of force/moment applied to the upper body of the robot by the male dancer. The proposed method is effective for the male dancer whom the teaching data are collected from, although the success rate of the step estimation with another male dancer is not always high. In this paper, a step estimation method for a dance partner robot is proposed which updates parameters of the hidden Markov model at each step transition and improves the success rate of the dance step estimation for any dance partner. Experimental results illustrate the effectiveness of the proposed method.