This paper studies the coupling of intrinsic motiva- tion and physiological maturational constraints, and argues that both mechanisms may have complex bidirectional interactions allowing to actively control the growth of complexity in motor development. First, we introduce the self-adaptive goal generation algorithm (SAGG), instantiating an intrinsically motivated goal exploration mechanism for motor learning of inverse models. Then, we introduce a functional model of maturational con- straints inspired by the myelination process in humans, and show how it can be coupled with the SAGG algorithm, forming a new system called McSAGG. We then present experiments to evaluate qualitative properties of these systems when applied to learning a reaching skill with an arm with initially unknown kinematics.