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The objective of this dissertation is to shed light on some fundamentalimpediments in learning control laws in continuous state spaces. In particular,if one wants to build artificial devices capable to learn motor tasks the sameway they learn to classify signals and images, one needs to establish controlrules that do not necessitate comparisons between quantities of the surroundingspace. We propose, in that context, to take inspiration from the "end effectorcontrol" principle, as suggested by neuroscience studies, as opposed to the"displacement control" principle used in the classical control theory.

Predicting the consequence of action in digital control state spaces