Abstract: Self-organization---ubiquitous in nature---is a major challenge for both artificial life and modern robotics offering intriguing perspectives for practical applications utilized so far only incipiently. There is some progress, though, in formulating general objective functions for driving systems into self-organization (SO). Based on general principles like information maximization, these approaches are domain invariant and free of arbitrariness. However, and this seems to be a major source of concerns, if nothing is specified from outside, will SO simply make the robot an arbitrary subject that is completely unpredictable in its behaviors and thus rather a thread than a hope. The aim of this paper is to show that this attitude is not justified. Instead, we develop an understanding of what happens if the system is self-organizing, what the role of the embodiment is and how we can find clues for predicting and shaping the behavior patterns emerging in a genuine SO scenario. The approach is based on a new unsupervised learning rule staging two antagonistic activities---driving systems towards instability while preserving the physical symmetries of the system as much as possible. This leads to spontaneous symmetry breaking, the leading phenomenon of SO known from nature that has been overlooked by the robotics community so far. It is shown by a number of examples that the unsupervised learning rule induces an amazing variety of behaviors---patterns in space and time that can be interpreted as broken symmetries.
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