Interactive Learning in Human-Robot Collaboration

Tetsuya Ogata*, Noritaka Masago, Shigeki Sugano, Jun Tani

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

9 Citations (Scopus)


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 net-work (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.

Original languageEnglish
Number of pages6
Publication statusPublished - 2003
Event2003 IEEE/RSJ International Conference on Intelligent Robots and Systems - Las Vegas, NV, United States
Duration: 2003 Oct 272003 Oct 31


Conference2003 IEEE/RSJ International Conference on Intelligent Robots and Systems
Country/TerritoryUnited States
CityLas Vegas, NV

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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