  We treat the problem of autonomous acquisition of manipulation skills where
problem-solving strategies are initially available only for a narrow range of
situations. We propose to extend the range of solvable situations by autonomous
playing with the object. By applying previously-trained skills and behaviours,
the robot learns how to prepare situations for which a successful strategy is
already known. The information gathered during autonomous play is additionally
used to learn an environment model. This model is exploited for active learning
and the creative generation of novel preparatory behaviours. We apply our
approach on a wide range of different manipulation tasks, e.g. book grasping,
grasping of objects of different sizes by selecting different grasping
strategies, placement on shelves, and tower disassembly. We show that the
creative behaviour generation mechanism enables the robot to solve
previously-unsolvable tasks, e.g. tower disassembly. We use success statistics
gained during real-world experiments to simulate the convergence behaviour of
our system. Experiments show that active improves the learning speed by around
9 percent in the book grasping scenario.
