Self-organized adaptation of a simple neural circuit enables complex robot behaviour
Silke Steingrube, Marc Timme, Florentin Wörgötter, and Poramate Manoonpong (2010)
Nature Physics 6:224-230. ( BibTeX export )
Structure, Dynamics and Function of Complex Networks,
Control and Selforganization for Autonomous Robots,
Theory of Precise Timing in Spiking Neural Networks
Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because
many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with
the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and
are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex
behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control
circuit, thereby generating 11 basic behavioural patterns (for example, orienting, taxis, self-protection and various gaits) and
their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic
long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to
self-organize versatile behaviours in autonomous agents with many degrees of freedom.
Article.
Advanced online version appeared January 17, 2010
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