HMM-based dance step estimation 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 this paper, especially, we improve an estimation system for dance steps, which estimates a next dance step intended by a human. For estimating the dance step, time series data of force/moment applied by a human to the robot are utilized. The time series data of force/moment measured during dancing by a human and the robot include the uncertainty such as time-lag and variations for each repeated trial, because a human can not always apply the same force/moment to the robot exactly. In order to treat the time series data including such uncertainty, hidden Markov models are utilized for designing the dance step estimation system. With the proposed system, the robot estimates a next dance step based on human intention successfully.