Motion recognition by combining HMM and reinforcement learning

Kazuhisa Hamamoto, Ken'ichi Morooka, Hiroshi Nagahashi

研究成果: 著書/レポートタイプへの貢献会議での発言

3 引用 (Scopus)

抜粋

It is difficult to give a robot all possible motions beforehand in a certain environment. Therefore, the robot needs to learn how to recognize others' motions and to generate its own motions autonomously for working well. These learning algorithms need an efficient way to make recognition and generation of motions work together, because they take many computing resources. This paper focuses on a generation-based recognition. Our system consists of recognition and generation modules. The former and latter are constructed from lefl-to-right Hidden Markov Models (HMM) and Reinforcement Learning (RL), respectively. When a HMM in recognition module does not work enough, the model parameters of HMM are re-estimated by using a state-value function of RL in generation module. The proposed method enables us to improve the reliability of the HMM.

元の言語英語
ホスト出版物のタイトル2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
ページ5259-5264
ページ数6
DOI
出版物ステータス出版済み - 12 1 2004
外部発表Yes
イベント2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, オランダ
継続期間: 10 10 200410 13 2004

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
6
ISSN(印刷物)1062-922X

その他

その他2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
オランダ
The Hague
期間10/10/0410/13/04

    フィンガープリント

All Science Journal Classification (ASJC) codes

  • Engineering(all)

これを引用

Hamamoto, K., Morooka, K., & Nagahashi, H. (2004). Motion recognition by combining HMM and reinforcement learning. : 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 (pp. 5259-5264). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; 巻数 6). https://doi.org/10.1109/ICSMC.2004.1401029