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Carnegie Mellon Robotics Institute

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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 autonomous and human 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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center, National Robotics Engineering Center, and Field Robotics Center

Associated Project(s): UGCV PerceptOR Integrated and CTA Robotics

Number of pages: 10

Text Reference

Boris Sofman, J. Andrew (Drew) Bagnell, and Anthony (Tony) Stentz, "Bandit-Based Online Candidate Selection for Adjustable Autonomy," 7th International Conferences on Field and Service Robotics, July, 2009.

BibTeX Reference

@inproceedings{Sofman_2009_6392,
   author = "Boris Sofman and J. Andrew (Drew) Bagnell and Anthony (Tony) Stentz",
   title = "Bandit-Based Online Candidate Selection for Adjustable Autonomy",
   booktitle = "7th International Conferences on Field and Service Robotics",
   month = "July",
   year = "2009",
}

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