  In open-ended continuous environments, robots need to learn multiple
parameterised control tasks in hierarchical reinforcement learning. We
hypothesise that the most complex tasks can be learned more easily by
transferring knowledge from simpler tasks, and faster by adapting the
complexity of the actions to the task. We propose a task-oriented
representation of complex actions, called procedures, to learn online task
relationships and unbounded sequences of action primitives to control the
different observables of the environment. Combining both goal-babbling with
imitation learning, and active learning with transfer of knowledge based on
intrinsic motivation, our algorithm self-organises its learning process. It
chooses at any given time a task to focus on; and what, how, when and from whom
to transfer knowledge. We show with a simulation and a real industrial robot
arm, in cross-task and cross-learner transfer settings, that task composition
is key to tackle highly complex tasks. Task decomposition is also efficiently
transferred across different embodied learners and by active imitation, where
the robot requests just a small amount of demonstrations and the adequate type
of information. The robot learns and exploits task dependencies so as to learn
tasks of every complexity.
