Developmental robotics studies and experiments mechanisms for autonomous life-long learning of skills in robots and humans. One of the crucial challenges is due to the sharp contrast between the high-dimensionality of their sensorimotor space and the limited number of physical experiments they can make within their life-time. This also includes the capability to adapt skills to changing environments or to novel tasks. To achieve efficient life-long learning in such complex spaces, humans benefit from various interacting developmental mechanisms which generally structure exploration from simple learning situations to more complex ones. I will present recent research in developmental robotics that has proposed several ways to transpose these developmental learning mechanisms to robots. In particular, I will present and discuss computational mechanisms of intrinsically motivated active learning, which automatically select training examples [4] or tasks [2] of increasing complexity, and their interaction with imitation learning [3], as well as maturation and body growth where the number of sensori and motor degrees-of-freedom evolve through phases of freezing and freeing