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.
|ジャーナル||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|出版ステータス||Published - 2004 1月 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月 17 → 2004 5月 20
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）