AUTOMATION 21XX

Company Profile

The world of self-organized creatures Self-organization is one of the most striking phenomena of our world. Even evolution may be seen as the way nature realized self-organization under the constraints of mortal beings in an eternal fight for resources. As a consequence evolution is an incremental process building on solutions once they are established. Robots can be thought free of these suffocating constraints. Instead they are potentially immortal and free of energy restraints. Then, our question is how to organize the self-organization in such a world opening for each robot the way for an individual open ended development. Our robots have a fixed morphology (no structural self-organization considered so far) with a \"brain\" consisting of two artificial neural networks, one for the control and another one for cognition, i.e. the \"understanding\" of the robots reactions to the controls. The essential point of our approach is that learning in both networks is self-supervised, driven by an objective function which is completely domain invariant, depending exclusively on the robots sensor values. The objective is mainly to make the robot sensitive so that small variations in sensor values induce large variations in motor values resulting in even larger sensorial responses and so on. This would drive the robot towards a hyperactive, chaotic behavior. The way into complete chaos is counteracted by both the physics of the robot itself (inertia, cross relations, ...) and the decline of understanding in the chaotic regime. As a solution of these conflicting effects the robot develops a kind of self-exploration of its bodily affordances in a more or less playful way with a tendency to development due to increasing cognitive abilities. We present below a number of examples demonstrating that this principle, called also the principle of homeokinesis, can be translated into a reliable, extremely robust algorithm which governs the parameter dynamics of the neural networks for both the self-model and the controller. Videos are from simulated environments as well as from real world experiments.