Acquiring mobile robot behaviors by learning trajectory velocities

Koren Ward, Alexander Zelinsky

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)

    Abstract

    The development of robots that learn from experience is a relentless challenge confronting artificial intelligence today. This paper describes a robot learning method which enables a mobile robot to simultaneously acquire the ability to avoid objects, follow walls, seek goals and control its velocity as a result of interacting with the environment without human assistance. The robot acquires these behaviors by learning how fast it should move along predefined trajectories with respect to the current state of the input vector. This enables the robot to perform object avoidance, wall following and goal seeking behaviors by choosing to follow fast trajectories near: the forward direction, the closest object or the goal location respectively. Learning trajectory velocities can be done relatively quickly because the required knowledge can be obtained from the robot's interactions with the environment without incurring the credit assignment problem. We provide experimental results to verify our robot learning method by using a mobile robot to simultaneously acquire all three behaviors.

    Original languageEnglish
    Pages (from-to)113-133
    Number of pages21
    JournalAutonomous Robots
    Volume9
    Issue number2
    DOIs
    Publication statusPublished - Sept 2000

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