Chang, CaroilnaGaudiano, Paolo2011-11-142011-11-141998-02https://hdl.handle.net/2144/2340We present a neural network that learns to control approach and avoidance behaviors in a mobile robot using the mechanisms of classical and operant 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 the Khepera and Pioneer 1 mobile robots.en-USCopyright 1998 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.Technical ReportBoston University Trustees