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

J. Yang, Yangsheng Xu, and M. Chen
Proceedings of the 1993 IEEE International Conference on Robotics and Automation (ICRA '93), May, 1993, pp. 396 - 402.


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Abstract
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.

Notes

Text Reference
J. Yang, Yangsheng Xu, and M. Chen, "Hidden Markov Model Approach to Skill Learning and Its Application to Telerobotics," Proceedings of the 1993 IEEE International Conference on Robotics and Automation (ICRA '93), May, 1993, pp. 396 - 402.

BibTeX Reference
@inproceedings{Xu_1993_3834,
   author = "J. Yang and Yangsheng Xu and M. Chen",
   title = "Hidden Markov Model Approach to Skill Learning and Its Application to Telerobotics",
   booktitle = "Proceedings of the 1993 IEEE International Conference on Robotics and Automation (ICRA '93)",
   pages = "396 - 402",
   month = "May",
   year = "1993",
   volume = "1",
}