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

Algorithms

Frank Hesse²  Georg Martius²  Ralf Der¹ 

[1] Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, GermanyMax Planck Institute for Dynamics and Self-Organization, Bunsenstrasse 10, 37073 Göttingen, Germany;

关键词: Self-Organization;    Autonomous Robot Control;    Neural Networks;    Homeokinesis.;   

DOI  :  10.3390/a2010398

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.