Hidden Markov Model Approach to Skill Learning and Its Application to Telerobotics

Abstract

In this paper, we discuss the problem of how human skill can be represented as a parametric model using a hidden Markov model (HMM), and how a HMM-based skill model can be used to learn human skill. HMM is feasible to characterize two stochastic processes?easurable action and immeasurable mental states?hich are involved in the skill learning. We formulated the learning problem as a multi-dimensional HMM and developed a programming system which serves as a skill learning testbed for a variety of applications. Based on "the most likely performance" criterion, we can select the best action sequence from all previously measured action data by modeling the skill as HMM. This selection process can be updated in real-time by feeding new action data and modifying HMM parameters. We address the implementation of the proposed method in a teleoperation-controlled space robot. An operator specifies the control command by a hand controller for the task of exchanging Orbit Replaceable Unit, and the robot learns the operation skill by selecting the sequence which represents the most likely performance of the operator. The skill is learned in Cartesian space, joint space, and velocity domain. The experimental results demonstrate the feasibility of the proposed method in learning human skill and teleoperation control. The learning is significant in eliminating sluggish motion and correcting the motion command which the operator mistakenly generates.