Accession Number : ADA266989


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


Descriptive Note : Technical rept.,


Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA ROBOTICS INST


Personal Author(s) : Yang, Jie ; Xu, Yangsheng ; Chen, C S


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a266989.pdf


Report Date : Jan 1993


Pagination or Media Count : 33


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--measurable action and immeasurable mental states--which 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.


Descriptors :   *SKILLS , *LEARNING , VELOCITY , MATHEMATICAL MODELS , CONTROL , HUMANS , COMPUTER PROGRAMMING , MOTION , SELECTION , TELEOPERATORS , HANDS , MARKOV PROCESSES , TIME , SEQUENCES , ROBOTS , PARAMETERS , REAL TIME , ORBITS , STOCHASTIC PROCESSES


Subject Categories : Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE