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A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior
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 timescales 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 timescales. 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.
@article{hesse:sensorbased09, title = {A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior}, journal = {Algorithms}, 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 timescales 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 timescales. 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.}, volume = {2}, number = {1}, pages = {398-409}, year = {2009}, slug = {hesse-sensorbased09}, author = {Hesse, Frank and Martius, Georg and Der, Ralf and Herrmann, J. Michael}, url = {http://www.mdpi.com/1999-4893/2/1/398} }