Open-end human robot interaction from the dynamical systems perspective: Mutual adaptation and incremental learning

Tetsuya Ogata*, Shigeki Sugano, Jun Tani

*この研究の対応する著者

研究成果: Conference article査読

11 被引用数 (Scopus)

抄録

This paper describes interactive learning between human subjects and robot using the dynamical systems approach. Our research concentrated on the navigation system of a humanoid robot and human subjects whose eyes were covered. We used the recurrent neural network (RNN) for the robot control. We used a "consolidation-learning algorithm" as a model of hippocampus in brain. In this method, the RNN was trained by both a new data and the rehearsal outputs of the RNN, not to damage the contents of current memory. The proposed method enabled the robot to improve the performance even when learning continued for a long time (open-end). The dynamical systems analysis of RNNs supports these differences.

本文言語English
ページ(範囲)435-444
ページ数10
ジャーナルLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
3029
DOI
出版ステータスPublished - 2004 1 1
イベント17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada
継続期間: 2004 5 172004 5 20

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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