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
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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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 = {} }