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 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. We 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 in certain tasks and to improve motion performance.


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    Titel :

    Hidden Markov model approach to skill learning and its application to telerobotics


    Weitere Titelangaben:

    Ansatz des verborgenen Markov-Modells für Lernprozesse und dessen Anwendung bei ferngesteuerten Robotern


    Beteiligte:
    Jie Yang (Autor:in) / Xu, Yangsheng (Autor:in) / Chen, C.S. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    1994


    Format / Umfang :

    11 Seiten, 30 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




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