Sensor Adaptation and Development in Robots by Entropy Maximization of Sensory Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

A method is presented for adapting the sensors of a robot to the statistical structure of its current environment. This enables the robot to compress incoming
sensory information and to find informational relationships between sensors. The method is applied to creating sensoritopic maps of the informational relationships of the sensors of a developing robot, where the informational distance between sensors is computed using information theory and
adaptive binning. The adaptive binning method constantly estimates the probability distribution of the latest inputs to maximize the entropy in each individual sensor, while conserving the correlations between different sensors. Results from simulations and robotic experiments with visual sensors
show how adaptive binning of the sensory data helps the system to discover structure not found by ordinary binning. This enables the developing perceptual system of the robot to be more adapted to the particular embodiment of the robot and the environment.
Original languageEnglish
Title of host publicationProcs of the 2005 IEEE Int Symposium on Computational Intelligence in Robotics and Automation
PublisherIEEE
Pages587-592
Publication statusPublished - 2005

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