  Acquiring new robot motor skills is cumbersome, as learning a skill from
scratch and without prior knowledge requires the exploration of a large space
of motor configurations. Accordingly, for learning a new task, time could be
saved by restricting the parameter search space by initializing it with the
solution of a similar task. We present a framework which is able of such
knowledge transfer from already learned movement skills to a new learning task.
The framework combines probabilistic movement primitives with descriptions of
their effects for skill representation. New skills are first initialized with
parameters inferred from related movement primitives and thereafter adapted to
the new task through relative entropy policy search. We compare two different
transfer approaches to initialize the search space distribution with data of
known skills with a similar effect. We show the different benefits of the two
knowledge transfer approaches on an object pushing task for a simulated 3-DOF
robot. We can show that the quality of the learned skills improves and the
required iterations to learn a new task can be reduced by more than 60% when
past experiences are utilized.
