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

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

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.

Keywords

Bayesian Network Bayesian Information Criterion Auditory Feedback Decision Node Visual Access 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Nathan Koenig
    • 1
  • Leila Takayama
    • 2
  • Maja J. Matarić
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.Willow GarageMenlo ParkUSA

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