  Imitation learning is a way to teach robots skills that are demonstrated by
humans. Transfering skills between these different kinematic structures seems
to be straightforward in Cartesian space. Because of the correspondence
problem, however, the result will most likely not be identical. This is why
refinement is required, for example, by policy search. Policy search in
Cartesian space is prone to reachability problems when using conventional
inverse kinematic solvers. We propose a configurable approximate inverse
kinematic solver and show that it can accelerate the refinement process
considerably. We also compare empirically refinement in Cartesian space and
refinement in joint space.
