  We propose an active learning architecture for robots, capable of organizing
its learning process to achieve a field of complex tasks by learning sequences
of motor policies, called Intrinsically Motivated Procedure Babbling (IM-PB).
The learner can generalize over its experience to continuously learn new tasks.
It chooses actively what and how to learn based by empirical measures of its
own progress. In this paper, we are considering the learning of a set of
interrelated tasks outcomes hierarchically organized. We introduce a framework
called 'procedures', which are sequences of policies defined by the combination
of previously learned skills. Our algorithmic architecture uses the procedures
to autonomously discover how to combine simple skills to achieve complex goals.
It actively chooses between 2 strategies of goal-directed exploration :
exploration of the policy space or the procedural space. We show on a simulated
environment that our new architecture is capable of tackling the learning of
complex motor policies, to adapt the complexity of its policies to the task at
hand. We also show that our 'procedures' framework helps the learner to tackle
difficult hierarchical tasks.

Human-Computer Interaction;Artificial Intelligence;Robotics