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Imitation learning is a way to teach robots skills that are demonstrated byhumans. Transfering skills between these different kinematic structures seemsto be straightforward in Cartesian space. Because of the correspondenceproblem, however, the result will most likely not be identical. This is whyrefinement is required, for example, by policy search. Policy search inCartesian space is prone to reachability problems when using conventionalinverse kinematic solvers. We propose a configurable approximate inversekinematic solver and show that it can accelerate the refinement processconsiderably. We also compare empirically refinement in Cartesian space andrefinement in joint space.

A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills