  We present an active learning architecture that allows a robot to actively
learn which data collection strategy is most efficient for acquiring motor
skills to achieve multiple outcomes, and generalise over its experience to
achieve new outcomes. The robot explores its environment both via interactive
learning and goal-babbling. It learns at the same time when, who and what to
actively imitate from several available teachers, and learns when not to use
social guidance but use active goal-oriented self-exploration. This is
formalised in the framework of life-long strategic learning. The proposed
architecture, called Socially Guided Intrinsic Motivation with Active Choice of
Teacher and Strategy (SGIM-ACTS), relies on hierarchical active decisions of
what and how to learn driven by empirical evaluation of learning progress for
each learning strategy. We illustrate with an experiment where a simulated
robot learns to control its arm for realising two kinds of different outcomes.
It has to choose actively and hierarchically at each learning episode: 1) what
to learn: which outcome is most interesting to select as a goal to focus on for
goal-directed exploration; 2) how to learn: which data collection strategy to
use among self-exploration, mimicry and emulation; 3) once he has decided when
and what to imitate by choosing mimicry or emulation, then he has to choose who
to imitate, from a set of different teachers. We show that SGIM-ACTS learns
significantly more efficiently than using single learning strategies, and
coherently selects the best strategy with respect to the chosen outcome, taking
advantage of the available teachers (with different levels of skills).
