Object Learning with Natural Language in a Distributed Intelligent System: A Case Study of Human-Robot Interaction

  • Stefan Heinrich
  • Pascal Folleher
  • Peer Springstübe
  • Erik Strahl
  • Johannes Twiefel
  • Cornelius Weber
  • Stefan Wermter
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

The development of humanoid robots for helping humans as well as for understanding the human cognitive system is of significant interest in science and technology. How to bridge the large gap between the needs of a natural human-robot interaction and the capabilities of recent humanoid platforms is an important but open question. In this paper we describe a system to teach a robot, based on a dialogue in natural language about its real environment in real time. For this, we integrate a fast object recognition method for the NAO humanoid robot and a hybrid ensemble learning mechanism. With a qualitative analysis we show the effectiveness of our system.

Keywords

Ensemble learning Human-robot interaction Language 

Notes

Acknowledgments

The authors would like to thank Sven Magg and Nils Meins for very inspiring as well as very helpful discussions. This work has been partially supported by the KSERA project funded by the European Commission under n\(^\circ \) 2010-248085 and by the RobotDoC project funded by Marie Curie ITN under 235065.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Stefan Heinrich
    • 1
  • Pascal Folleher
    • 1
  • Peer Springstübe
    • 1
  • Erik Strahl
    • 1
  • Johannes Twiefel
    • 1
  • Cornelius Weber
    • 1
  • Stefan Wermter
    • 1
  1. 1.Department of Informatics, Knowledge TechnologyUniversity of HamburgHamburgGermany

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