Abstract: Embodiment and self-organization (SO) form two cornerstones of both modern robotics and the understanding of human and animal intelligence. While the role of the embodiment for the behavior has become of much and increasing interest in recent times, the attitude of the community toward SO is still characterized by some reservations. These concern on the one hand the question how a robotic system can be made to self-organize its behavior in a systematic and transparent manner. There is some progress in that direction by several objective functions for SO and learning rules derived thereof. Based on general principles, these approaches are domain invariant and free of arbitrariness. However, and this seems to be the main argument, 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 threat than a hope. The aim of this paper is to show that this attitude is not justified. Instead, we (i) show how a system can be made to self-organize its behavior, we (ii) develop an understanding of what happens if the system is self-organizing, highlight (iii) what the role of the embodiment is and give (iv) clues for predicting and shaping the behavior patterns emerging in an SO scenario. The approach is based on a new unsupervised learning rule staging two antagonistic activities---driving systems towards instability while conserving the geometric and physical symmetries of the system as much as possible. This leads to a spontaneous symmetry breaking scenario, the guiding phenomenon of SO known from nature. 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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