In this papel: we investigated interactive learning between humon subjects and robot experimentally, and its essential characteristics ore examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects wliose eyes were covered, making them dependent on the robot f o r directions. We compared the usual feed-forword neural network (FFNN) without recursive connections and the recurrent neural network (R"). Although theperformances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own. all performances gradually become unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operabili@. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the perfarmonce improved even when interactive learning continued for a long time.The questionnaire results then also confirmed that the subject's mental impressions of the R" improved significantly. The dynamical systems analysis of RNNs support these differences.
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