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Bayesian Programming and Hierarchical Learning in Robotics

By Julien Diard and Olivier Lebeltel

Abstract

This paper presents a new robotic programming environment based on the probability calculus. We show how reactive behaviours, like obstacle avoidance, contour following, or even light following, can be programmed and learned by a Khepera robot with our system. We further demonstrate that behaviours can be combined either by programmation or learning. A homing behaviour is thus obtained by combining obstacle avoidance and light following

Topics: [SCCO.COMP]Cognitive science/Computer science
Publisher: HAL CCSD
Year: 2000
OAI identifier: oai:HAL:hal-00019361v1
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