This paper studies computational models of the coupling of intrinsic motivations and physiological maturational constraints, and argues that both mechanisms may have complex bidirectional interactions allowing the active control of the growth of complexity in motor development which directs an efficient learning and exploration process. First, we outline the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity algorithm (SAGG-RIAC) that instantiates an intrinsically motivated goal exploration mechanism for motor learning of inverse models. Then, we introduce a functional model of maturational constraints inspired by the myelination process in humans, and show how it can be coupled with the SAGG-RIAC algorithm, forming a new system called McSAGG-RIAC2. We then present experiments to evaluate qualitative and, more importantly, quantitative properties of these systems when applied to a 12DOF quadruped controlled with 24 dimensions motor synergies.
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