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This work presents a novel learning method in the context of embodiedartificial intelligence and self-organization, which has as few assumptions andrestrictions as possible about the world and the underlying model. The learningrule is derived from the principle of maximizing the predictive information inthe sensorimotor loop. It is evaluated on robot chains of varying length withindividually controlled, non-communicating segments. The comparison of theresults shows that maximizing the predictive information per wheel leads to ahigher coordinated behavior of the physically connected robots compared to amaximization per robot. Another focus of this paper is the analysis of theeffect of the robot chain length on the overall behavior of the robots. It willbe shown that longer chains with less capable controllers outperform those ofshorter length and more complex controllers. The reason is found and discussedin the information-geometric interpretation of the learning process.

Higher coordination with less control - A result of information maximization in the sensorimotor loop