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Algorithms 2009, 2(1), 398-409; doi:10.3390/a2010398

A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior

1, 2, 3,* , 1, 2,.3
1, 2, 5
Max-Planck-Institute for Dynamics and Self-Organization, Bunsenstrasse 10, 37073 Göttingen, Germany
Bernstein Center for Computational Neuroscience Göttingen, Bunsenstrasse 10, 37073 Göttingen, Germany
Institute for Nonlinear Dynamics, Georg-August-University Göttingen, Bunsenstrasse 10, 37073 Göttingen, Germany
Max-Planck-Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany
University of Edinburgh, School of Informatics, IPAB, 10 Crichton Street, Edinburgh, EH8 9AB, UK
Author to whom correspondence should be addressed.
Received: 30 November 2008 / Accepted: 26 February 2009 / Published: 4 March 2009
(This article belongs to the Special Issue Neural Networks and Sensors)
View Full-Text   |   Download PDF [2618 KB, uploaded 13 March 2009]   |  


Ideally, sensory information forms the only source of information to a robot. We consider an algorithm for the self-organization of a controller. At short time scales the controller is merely reactive but the parameter dynamics and the acquisition of knowledge by an internal model lead to seemingly purposeful behavior on longer time scales. As a paradigmatic example, we study the simulation of an underactuated snake-like robot. By interacting with the real physical system formed by the robotic hardware and the environment, the controller achieves a sensitive and body-specific actuation of the robot. View Full-Text
Keywords: Self-Organization; Autonomous Robot Control; Neural Networks; Homeokinesis Self-Organization; Autonomous Robot Control; Neural Networks; Homeokinesis

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Hesse, F.; Martius, G.; Der, R.; Herrmann, J.M. A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior. Algorithms 2009, 2, 398-409.

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