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Learning from Demonstration: A Study of Visual and Auditory Communication and Influence Diagrams

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Experimental Robotics

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 79))

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Abstract

Learning from demonstration utilizes human expertise to program a robot. We believe this approach to robot programming will facilitate the development and deployment of general purpose personal robots that can adapt to specific user preferences. Demonstrations can potentially take place across a wide variety of environmental conditions. In this paper we study the impact that the users visual access to the robot, or lack thereof, has on on teaching performance. Based on the obtained results, we then address how a robot can provide additional information to a instructor during the LfD process, to optimize the two-way process of teaching and learning. Finally, we describe a novel Bayesian approach to generating task policies from demonstration data.

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Author information

Authors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Nathan Koenig & Maja J. Matarić

  2. Willow Garage, Menlo Park, CA, USA

    Leila Takayama

Authors
  1. Nathan Koenig
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  2. Leila Takayama
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  3. Maja J. Matarić
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Corresponding author

Correspondence to Nathan Koenig .

Editor information

Editors and Affiliations

  1. Artificial Intelligence Laboratory, Stanford University Dept. Computer Science, Stanford, California, USA

    Oussama Khatib

  2. Department of Mechanical Engineering, University of Pennsylvania GRASP Laboratory, Philadelphia, Pennsylvania, USA

    Vijay Kumar

  3. Department of Computer Science, University of Southern California MC 290, California, USA

    Gaurav Sukhatme

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Koenig, N., Takayama, L., Matarić, M.J. (2014). Learning from Demonstration: A Study of Visual and Auditory Communication and Influence Diagrams. In: Khatib, O., Kumar, V., Sukhatme, G. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28572-1_5

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  • DOI: https://doi.org/10.1007/978-3-642-28572-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28571-4

  • Online ISBN: 978-3-642-28572-1

  • eBook Packages: EngineeringEngineering (R0)

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