Structure Evolution for Neural Behavior Control of Autonomous Systems

Evolutionary robotics in the context of recurrent neural networks seems to be a promising approach to demonstrate and test the relevance of complex internal dynamics of neural systems for nonlinear control problems. An evolutionary algorithm, called ENS^3 (evolution of neural systems by stochastic synthesis), has been successfully applied for behavior control of divers robot platforms and tasks.

The ENS^3 algorithm is applied to networks of standard additive neurons with sigmoidal transfer function and it is designed to generate recurrent architectures allowing complex dynamics. In this talk experiments of the following domain are presented: robust behavior control, sensor fusion and environment representation for differential wheel driven robots; obstacle avoidance controller for an omni-directional robot platform; sensor and behavior fusion in the domain of RoboCup Middle Size League; evolution of morphology and neuro-controller for a biped walking machine; co-evolution experiments in the micro.adam / micro.eva art project of Julius Popp.