著者

西川 輝彦 港 隆史 荻野 正樹 浅田 稔

出版者

一般社団法人 日本機械学会

雑誌

ロボティクス・メカトロニクス講演会講演概要集 2011 (ISSN:24243124)

巻号頁・発行日

pp._2P2-M02_1-_2P2-M02_4, 2011-05-26 (Released:2017-06-19)

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This paper proposes a hierarchical model which is composed of a slow feature analysis (SFA) network to extract multi-modal representation of a humanoid robot. The experiment with humanoid robot shows that the network can integrate multi-modal information and detect semantic features by the extraction of the slowly varying features from the high-dimensional input sensory signal, and it shows that the multi-modal representation is useful as state representation for reinforcement learning compared with using state representation without the integration of the multi-modal information.