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.
Highlights
A Sensor-Based Learning Algorithm for the Self-Organization ofFrank Hesse 1,2,3,? , Georg Martius 1,2,3 , Ralf Der 4 and J
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
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
Summary
Frank Hesse 1,2,3,? , Georg Martius 1,2,3 , Ralf Der 4 and J. Received: 30 November 2008 / Accepted: 26 February 2009 / Published: 4 March 2009
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