  In complex manipulation scenarios (e.g. tasks requiring complex interaction
of two hands or in-hand manipulation), generalization is a hard problem.
Current methods still either require a substantial amount of (supervised)
training data and / or strong assumptions on both the environment and the task.
In this paradigm, controllers solving these tasks tend to be complex. We
propose a paradigm of maintaining simpler controllers solving the task in a
small number of specific situations. In order to generalize to novel
situations, the robot transforms the environment from novel situations into a
situation where the solution of the task is already known. Our solution to this
problem is to play with objects and use previously trained skills (basis
skills). These skills can either be used for estimating or for changing the
current state of the environment and are organized in skill hierarchies. The
approach is evaluated in complex pick-and-place scenarios that involve complex
manipulation. We further show that these skills can be learned by autonomous
playing.
