Michael Beetz and Thorsten Belker

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

Proceedings of the IJCAI Workshop on Robot Action Planning

---

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. XfrmLearn 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.

---

Download

Full paper [.pdf] (586 kB)

---

Bibtex

@InProceedings{Beetz99IJCAIWS,
  author =       {M. Beetz and T. Belker},
  title =        {Experience- and Model-based Transformational Learning of Symbolic Behavior Specifications -- Prelimary Report},
  booktitle =    {Proceedings of the IJCAI Workshop on Robot Action Planning},
  OPTpages =	 {1-7},
  year =         {1999},
}