Hidden Markov model approach to skill learning and its application to telerobotics
- Carnegie Mellon Univ., Pittsburgh, PA (United States). Robotics Inst. Univ. of Akron, OH (United States). Dept. of Electrical Engineering
- Carnegie Mellon Univ., Pittsburgh, PA (United States). Robotics Inst.
- Univ. of Akron, OH (United States). Dept. of Electrical Engineering
In this paper, the authors discuss the problem of how human skill can be represented as a parametric model using a hidden Markov model (HMM), and how an HMM-based skill model can be used to learn human skill. HMM is feasible to characterize a doubly stochastic process--measurable action and immeasurable mental states--that is involved in the skill learning. The authors formulated the learning problem as a multidimensional HMM and developed a testbed for a variety of skill learning applications. Based on ''the most likely performance'' criterion, the best action sequence can be selected from all previously measured action data by modeling the skill as an HMM. The proposed method has been implemented in the teleoperation control of a space station robot system, and some important implementation issues have been discussed. The method allows a robot to learn human skill certain tasks and to improve motion performance.
- OSTI ID:
- 6889121
- Journal Information:
- IEEE Transactions on Robotics and Automation (Institute of Electrical and Electronics Engineers); (United States), Vol. 10:5; ISSN 1042-296X
- Country of Publication:
- United States
- Language:
- English
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