Self-organized adaptation of simple neural circuits enables complex robot behavior

The control of complex sensori-motor systems is a challenging combinatorial problem because multiple simultaneous sensory signals need to be appropriately coordinated to yield a broad spectrum of distinct behavioral patterns. In this talk I will present a novel strategy to adaptively generate complex behavior of an autonomous robot using chaos-control in only one simple two-neuron module. The robot is sensor-driven by 18 inputs, which via a control network target 18 motors, thereby generating eleven basic behavioral patterns (e.g., orienting, taxis, self-protection, various gaits) and their combinations. The control strategy is adaptive and freely configurable by synaptic learning and thus provides an efficient yet simple way to self-organize versatile behaviors in autonomous systems with many degrees of freedom.