Self-organization of behavior for autonomous robots

The talk discusses how general principles of self-organization, like information maximization, can be realized in practice. We consider embodied robots with 10 - 30 independent degrees of freedom controlled by a neural network. Under a closed coupling paradigm, the behavior of the robot is defined by the several hundred parameters of that network. Behavior development is defined as a gradient flow on objective functions obtained from both information and dynamical systems theory. We describe techniques and methods how the gradients can be estimated in these extremely high parameter spaces in real time. Results are demonstrated by a number of robotic applications.