Dance step estimation method based on HMM for dance partner robot

The main purpose of this paper is to realize an effective human-robot coordination with physical interaction. A dance partner robot has been proposed as a platform for it. To realize the effective human-robot coordination, recognizing human intention would be one of the key issues. This paper focuses on an estimation method for dance steps, which estimates a next dance step intended by a human. In estimating the dance step, time series data of force/moment applied by the human to the robot are used. The time series data of force/moment measured in dancing include uncertainty such as time lag and variations for repeated trials because the human could not always exactly apply the same force/moment to the robot. In order to treat the time series data including such uncertainty, hidden Markov models are utilized for designing the dance step estimation method. With the proposed method, the robot successfully estimates a next dance step based on human intention.