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