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Discovering Motion Flow by Temporal-Informational Correlations in Sensors

By Lars Olsson, Chrystopher L. Nehaniv and Daniel Polani

Abstract

A method is presented for adapting the sensors of a robot to its current environment and to learn motion flow detection by observing the informational relations between sensors and actuators. Examples are shown where the robot learns to detect motion flow from sensor data generated by its own movement

Topics: Statistical Models, Machine Learning, Robotics
Publisher: Lund University Cognitive Studies
Year: 2005
OAI identifier: oai:cogprints.org:4983

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Citations

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