The problem of how human skill can be represented as a parametric model using a hidden Markov (HMM), and how an HMM-based skill model can be used to learn human skill, is discussed. The HMM is feasible for characterizing two stochastic processes, measurable action and immeasurable mental states that are involved in the skill learning. Based on the most likely performance criterion, the best action sequence can be selected from previously measured action data by modeling the skill as an HMM. This selection process can be updated in real-time by feeding new action data and modifying HMM parameters. The implementation of the proposed method in a teleoperation-controlled space robot is discussed. The results demonstrate the feasibility of the method.
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