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

被引：9
|
作者：
Hesse, Frank [1 ,2 ,3 ]
Martius, Georg [1 ,2 ,3 ]
Der, Ralf [4 ]
Herrmann, J. Michael [1 ,2 ,5 ]
机构：
[1] Max Planck Inst Dynam & Self Org, Bunsenstr 10, Gottingen 37073, Germany
[2] Bernstein Ctr Computat Neurosci Gottingen, Gottingen 37073, Germany
[3] Georg August Univ Gottingen, Inst Nonlinear Dynam, Gottingen 37073, Germany
[4] Max Planck Inst Math Sci, Leipzig 04103, Germany
[5] Univ Edinburgh, Sch Informat, IPAB, Edinburgh EH8 9AB, Midlothian, Scotland
来源：
ALGORITHMS | 2009年 / 2卷 / 01期
关键词：
Self-Organization; Autonomous Robot Control; Neural Networks; Homeokinesis;
D O I：
10.3390/a2010398
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：398 / 409
页数：12
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