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Bandit-Based Online Candidate Selection for Adjustable Autonomy

  • Boris Sofman
  • J. Andrew Bagnell
  • Anthony Stentz
Conference paper
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 62)

Abstract

In many robot navigation scenarios, the robot is able to choose between some number of operating modes. One such scenario is when a robot must decide how to trade-off online between human and tele-operation control. When little prior knowledge about the performance of each operator is known, the robot must learn online to model their abilities and be able to take advantage of the strengths of each. We present a bandit-based online candidate selection algorithm that operates in this adjustable autonomy setting and makes choices to optimize overall navigational performance. We justify this technique through such a scenario on logged data and demonstrate how the same technique can be used to optimize the use of high-resolution overhead data when its availability is limited.

Keywords

Mobile Robot Online Algorithm Candidate Selection Bandit Problem Traversal Time 
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 Berlin Heidelberg 2010

Authors and Affiliations

  • Boris Sofman
    • 1
  • J. Andrew Bagnell
    • 1
  • Anthony Stentz
    • 1
  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburgh

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