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In this article, we apply the Free-Energy Principle to the question of motorprimitives learning. An echo-state network is used to generate motortrajectories. We combine this network with a perception module and a controllerthat can influence its dynamics. This new compound network permits theautonomous learning of a repertoire of motor trajectories. To evaluate therepertoires built with our method, we exploit them in a handwriting task whereprimitives are chained to produce long-range sequences.

Autonomous learning and chaining of motor primitives using the Free Energy Principle