A Lifelong Learning Perspective for Mobile Robot Navigation

Sebastian Thrun

Designing robots that learn by themselves to perform complex real-world tasks is a still-open challenge for the field of Robotics and Artificial Intelligence. In this paper we present the robot learning problem as a lifelong problem, in which a robot faces a collection of tasks over its entire lifetime. Such a scenario provides the opportunity to gather general-purpose knowledge that transfers across tasks. We illustrate a particular learning mechanism, explanation-based neural network learning, that transfers knowledge between related tasks via neural network action models. The learning approach is illustrated using a mobile robot, equipped with visual, ultrasonic and laser sensors. In less than 10 minutes operation time, the robot is able to learn to navigate to a marked target object in a natural office environment.

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@INCOLLECTION{Thrun95k,
  AUTHOR         = {S. Thrun},
  YEAR           = {1995},
  TITLE          = {A Lifelong Learning Perspective for Mobile Robot Control},
  BOOKTITLE      = {Intelligent Robots and Systems},
  EDITOR         = {V. Graefe},
  PUBLISHER      = {Elsevier}
}