{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:39:47Z","timestamp":1760243987763,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2009,3,4]],"date-time":"2009-03-04T00:00:00Z","timestamp":1236124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a2010398","type":"journal-article","created":{"date-parts":[[2009,3,4]],"date-time":"2009-03-04T12:14:06Z","timestamp":1236168846000},"page":"398-409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior"],"prefix":"10.3390","volume":"2","author":[{"given":"Frank","family":"Hesse","sequence":"first","affiliation":[{"name":"Max-Planck-Institute for Dynamics and Self-Organization, Bunsenstrasse 10, 37073 G\u00f6ttingen, Germany"},{"name":"Bernstein Center for Computational Neuroscience G\u00f6ttingen, Bunsenstrasse 10, 37073 G\u00f6ttingen, Germany"},{"name":"Institute for Nonlinear Dynamics, Georg-August-University G\u00f6ttingen, Bunsenstrasse 10, 37073 G\u00f6ttingen, Germany"}]},{"given":"Georg","family":"Martius","sequence":"additional","affiliation":[{"name":"Max-Planck-Institute for Dynamics and Self-Organization, Bunsenstrasse 10, 37073 G\u00f6ttingen, Germany"},{"name":"Bernstein Center for Computational Neuroscience G\u00f6ttingen, Bunsenstrasse 10, 37073 G\u00f6ttingen, Germany"}]},{"given":"Ralf","family":"Der","sequence":"additional","affiliation":[{"name":"Max-Planck-Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany"}]},{"given":"J. Michael","family":"Herrmann","sequence":"additional","affiliation":[{"name":"Max-Planck-Institute for Dynamics and Self-Organization, Bunsenstrasse 10, 37073 G\u00f6ttingen, Germany"},{"name":"Bernstein Center for Computational Neuroscience G\u00f6ttingen, Bunsenstrasse 10, 37073 G\u00f6ttingen, Germany"},{"name":"University of Edinburgh, School of Informatics, IPAB, 10 Crichton Street, Edinburgh, EH8 9AB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2009,3,4]]},"reference":[{"key":"ref_1","first-page":"43","article-title":"Homeokinesis - A new principle to back up evolution with learning","volume":"Vol. 55","author":"Der","year":"1999","journal-title":"Computational Intelligence for Modelling, Control, and Automation"},{"key":"ref_2","unstructured":"Dillman, R., Schraft, R. D., and W\u00f6rn, H. (2002). 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