@misc{cogprints3336, volume = {101}, editor = {Christopher G. Prince and Luc Berthouze and Hideki Kozima and Daniel Bullock and Georgi Stojanov and Christian Balkenius}, title = {Collaboration Development through Interactive Learning between Human and Robot}, author = {Tetsuya Ogata and Noritaka Masago and Shigeki Sugano and Jun Tani}, publisher = {Lund University Cognitive Studies}, year = {2003}, pages = {99--106}, keywords = {human-robot interaction, feed-forward neural network, recurrent neural network, dynamical systems analysis}, url = {http://cogprints.org/3336/}, abstract = {In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are 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 whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances 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 became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions.} }