Hybrid learning strategy to solve pendulum swing-up problem for real hardware

Shingo Nakamura*, Shuji Hashimoto

*この研究の対応する著者

    研究成果: Conference contribution

    3 被引用数 (Scopus)

    抄録

    In this paper, we propose a machine learning strategy to obtain the optimal controller for actual machine using hybrid platforms; real hardware and simulator. A simulator consists of the neural networks which directly can learn actual behaviors of the latest hardware and emulates them without physical modeling. On the other hand, the controller of the hardware is trained with the simulator by the reinforcement learning method to realize the optimal control for the target task, and applied to the real hardware. Then, as long as the iteration of these processes is simultaneously performed, the system can automatically generate the optimal controller without any works even when hardware constitution is changed or switched. In this manner, the real hardware and the simulator affect each other to make the system adaptable. Furthermore, in the processes of sampling and supplying hardware data, we put a buffering component. It keeps the latest data of the hardware and supplies non-biased data to the simulator. As an example of the proposal method, we pick up the pendulum swing-up problem. In the experiments, firstly, the optimization process performs step by step for the initial hardware constitution and the basic idea of the method is evaluated. Afterward, by changing a pendulum, we confirm system can autonomously generate the new optimal controller for the real hardware without any human operations.

    本文言語English
    ホスト出版物のタイトル2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
    ページ1972-1977
    ページ数6
    DOI
    出版ステータスPublished - 2008
    イベント2007 IEEE International Conference on Robotics and Biomimetics, ROBIO - Yalong Bay, Sanya
    継続期間: 2007 12月 152007 12月 18

    Other

    Other2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
    CityYalong Bay, Sanya
    Period07/12/1507/12/18

    ASJC Scopus subject areas

    • 人工知能
    • 制御およびシステム工学
    • 生体材料

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