Modeling of the peg-in-hole task based on impedance parameters and HMM

When we apply an impedance control to execute any tasks, it is very important how to decide the impedance parameters to realize the desired task. If we can extract impedance parameters from human teaching data as characteristics of the human skill, it is appropriate to use them for control because of the similarity between an impedance control and a human fingertips control. However, there often exists unevennesses in time and space in human data. Modeling with hidden Markov model (HMM) is one of the promising technique to construct an efficient model for time-variant data including unevennesses. HMM is capable of characterizing a doubly stochastic process with an underlying immeasurable stochastic process which can be measured through another set of stochastic processes. Therefore, the probabilistic modeling of certain time series data which includes unevennesses caused by the human is possible. In this paper, we propose a method to model the series of impedance parameters identified from human teaching data with HMM in order to extract an essential discrete model which expresses the human skill. In addition, some applications of the obtained model to robot control and skill evaluation are shown