Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors

Carolina Chang and Paolo Gaudiano
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Carolina Chang: Boston University Neurobotics Lab, Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 2215, USA
Paolo Gaudiano: Boston University Neurobotics Lab, Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 2215, USA

Advances in Complex Systems (ACS), 1998, vol. 01, issue 01, 79-114

Abstract: We present a neural network that learns to control approach and avoidance behaviors in a mobile robot based on a form of animal learning known asoperant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot's sensors. In this article we provide a detailed presentation of the model, and show our results with theKheperaandPioneer 1mobile robots.

Keywords: Robot learning; operant conditioning; neural networks; obstacle avoidance; approach behavior (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1142/S0219525998000065

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