_Algorithms_**2009**, 2, 398-409; DOI: 10.3390/a2010398  
OPIN-ACGRESS  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior**  
**[PERSON] \({}^{1,2,3,\star}\), [PERSON] \({}^{1,2,3}\), [PERSON] \({}^{4}\) and [PERSON] \({}^{1,2,5}\)**  
\({}^{1}\) Max Planck Institute for Dynamics and Self-Organization, Bunsenstrasse 10, 37073 Gottingen, Germany;  
\({}^{2}\) Bernstein Center for Computational Neuroscience Gottingen, Bunsenstrasse 10, 37073 Gottingen, Germany  
\({}^{3}\) Institute for Nonlinear Dynamics, Georg-August-University Gottingen, Bunsenstrasse 10, 37073 Gottingen, Germany  
\({}^{4}\) Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany  
\({}^{5}\) University of Edinburgh, School of Informatics, IPAB, 10 Crichton Street, Edinburgh, EH8 9 AB, U.K.  
E-Mail: [EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed. _Received: 30 November 2008 / Accepted: 26 February 2009 / Published: 4 March 2009_  
**Abstract:** 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. **Keywords:** Self-Organization; Autonomous Robot Control; Neural Networks; Homeokinesis. Introduction  
Despite the tremendous progress in hardware and in both sensorial and computational efficiency, the performance of contemporary autonomous robots does not reach that of simple animals. This situation has triggered an intense search for alternative approaches to the control of robots. The present paper exemplifies homeokinesis [1; 2], a general approach to the self-organization of behavior which is based on the dynamical systems approach to robot control cf. e.g. [3; 4; 5] and may be understood as a dynamical counterpart of the principle of homeostasis. According to the homeostatic principle [6; 7], behavior results from the compensation of perturbations of an internal homeostatic equilibrium. Although this approach proves successful in simple systems [8; 9], it remains elusive how it scales up to situations, where, e.g., internal nutrient levels in an agent are to give rise to the specific form of a resulting complex behavior. Homeokinesis, in contrast, provides a mechanism for the self-organization of elementary movements that are not directly caused by a triggering stimulus, but are generated by amplification of intrinsic or extrinsic noise which is counterbalanced by the simultaneous maximization of the predictability of the resulting behavior. Homeokinesis provides a mechanism to produce behaviors that are not brought about by a reward signal or a prescribed target state, but that minimize an internal error functional based on sensory information [10]. We will investigate a robotic device with a complex physical behavior that is controlled according to the homeokinetic principle. Starting with an inactive robot and an unspecifically initialized controller, movements are generated in the beginning by amplifying noisy sensory inputs. At the same time the parameters of the controller are adapted according to the changes in the sensor values that are observed following the execution of actions. In the course of the adaptation, the generated actions increasingly match the properties of the robotic device in its environment as observed by the sensors.