An adaptive robot motivational system

  • Authors:
  • George Konidaris;Andrew Barto

  • Affiliations:
  • Autonomous Learning Laboratory, Department of Computer Science, University of Massachusetts at Amherst;Autonomous Learning Laboratory, Department of Computer Science, University of Massachusetts at Amherst

  • Venue:
  • SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
  • Year:
  • 2006

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Abstract

We present a robot motivational system design framework The framework represents the underlying (possibly conflicting) goals of the robot as a set of drives, while ensuring comparable drive levels and providing a mechanism for drive priority adaptation during the robot's lifetime The resulting drive reward signals are compatible with existing reinforcement learning methods for balancing multiple reward functions We illustrate the framework with an experiment that demonstrates some of its benefits.