Michael Beetz and Thorsten Belker

Experience- and Model-based Transformational Learning of Symbolic Behavior Specifications - Preliminary Report

Technical Report IAI-TR-99-3, University of Bonn

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Abstract

The paper describes Xfrml, a system that learns symbolic behavior specifications to control and improve the continuous sensor-driven navigation behavior of an autonomous mobile robot. The robot is to navigate between a set of predefined locations in an office environment and employs a navigation system consisting of a path planner and a reactive collision avoidance system. Xfrml rationally reconstructs the continuous sensor-driven navigation behavior in terms of task hierarchies by identifying significant structures and commonalities in behaviors. It also constructs a statistical behavior model for typical navigation tasks. The behavior model together with a model of how the collision avoidance module should `perceive' the environment is used to detect behavior `flaws', diagnose them, and revise the plans to improve their performance. The learning method is implemented on an autonomous mobile robot.

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Full paper [.pdf] (586 kB)

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Bibtex

@TechReport{Beetz99TR,
  author =       {M. Beetz and T. Belker},
  title =        {Experience- and Model-based Transformational Learning of Symbolic Behavior Specifications -- Prelimary Report},
  institution =  {University of Bonn},
  year =         {1999},
  OPTkey =       {},
  OPTtype =      {},
  number =       {IAI-TR-99-3},
  OPTaddress =   {},
  OPTmonth =     {},
  OPTnote =      {},
  OPTannote =    {}
}