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Toward Human-Like Robot Learning

  • Sergei Nirenburg
  • Marjorie McShane
  • Stephen Beale
  • Peter Wood
  • Brian Scassellati
  • Olivier Magnin
  • Alessandro Roncone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10859)

Abstract

We present an implemented robotic system that learns elements of its semantic and episodic memory through language interaction with its human users. This human-like learning can happen because the robot can extract, represent and reason over the meaning of the user’s natural language utterances. The application domain is collaborative assembly of flatpack furniture. This work facilitates a bi-directional grounding of implicit robotic skills in explicit ontological and episodic knowledge and of ontological symbols in the real-world actions by the robot. In so doing, this work provides an example of successful integration of robotic and cognitive architectures.

Keywords

Learning based on language understanding Integration of robotic and cognitive architectures Memory management in artificial intelligent agents 

Notes

Acknowledgements

This work was supported in part by Grant N00014-17-1-221 from the U.S. Office of Naval Research. Any opinions or findings expressed in this material are those of the authors and do not necessarily reflect the views of the Office of Naval Research.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sergei Nirenburg
    • 1
  • Marjorie McShane
    • 1
  • Stephen Beale
    • 1
  • Peter Wood
    • 1
  • Brian Scassellati
    • 2
  • Olivier Magnin
    • 2
  • Alessandro Roncone
    • 2
  1. 1.Language-Endowed Intelligent Agents LabRensselaer Polytechnic InstituteTroyUSA
  2. 2.Social Robotics LabYale UniversityNew HavenUSA

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