A GOAL-ORIENTATION FRAMEWORK FOR SELF-ORGANIZING CONTROL

Frank Hesse () and Florentin Wörgötter ()
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Frank Hesse: Bernstein Center for Computational Neuroscience Göttingen and III. Physics Institute - Biophysics, Georg-August-Universität, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany
Florentin Wörgötter: Bernstein Center for Computational Neuroscience Göttingen and III. Physics Institute - Biophysics, Georg-August-Universität, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany

Advances in Complex Systems (ACS), 2013, vol. 16, issue 02n03, 1-14

Abstract: Self-organization, especially in the framework of embodiment in biologically inspired robots, allows the acquisition of behavioral primitives by autonomous robots themselves. However, it is an open question how self-organization of basic motor primitives and goal-orientation can be combined, which is a prerequisite for the usefulness of such systems. In the paper at hand we propose a goal-orientation framework allowing the combination of self-organization and goal-orientation for the control of autonomous robots in a mutually independent fashion. Self-organization based motor primitives are employed to achieve a given goal. This requires less initial knowledge about the properties of robot and environment and increases adaptivity of the overall system. A combination of self-organization and reward-based learning seems thus a promising route for the development of adaptive learning systems.

Keywords: Self-organization; reinforcement learning; autonomous robots (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0219525913500021

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