Acquisition of the Human Skill with Hidden Markov Model

Abstract

When we apply impedance control to execute any tasks, it is very important how to decide the impedance parameter to realize the desired task. If we can extract the impedance parameter from human teaching data as characteristic of the human skill, it is appropriate to use it for control because of the similarity between impedance control and human fingertips control. However, there often exists an unevenness 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. We propose a method to model the impedance parameter sequence 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 proposed.