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The existence of modular structures in the biological world strongly suggests that the training of this kind of structures is actually feasible. It is a key indication for the development of neural networks applications, especially in the field of robotics. Indeed, a single network can only efficiently treat problems with few independent variables; the combining of several networks is necessary to address more complex tasks. We investigate learning techniques and show that using a particulardoi:10.1109/iros.2000.894583 dblp:conf/iros/BuesslerU00 fatcat:iiiazx2upvchlnsjt4avqjq6ku