  In this work we show how to use the Operational Space Control framework (OSC)
under joint and cartesian constraints for reinforcement learning in cartesian
space. Our method is therefore able to learn fast and with adjustable degrees
of freedom, while we are able to transfer policies without additional dynamics
randomizations on a KUKA LBR iiwa peg in-hole task. Before learning in
simulation starts, we perform a system identification for aligning the
simulation environment as far as possible with the dynamics of a real robot.
Adding constraints to the OSC controller allows us to learn in a safe way on
the real robot or to learn a flexible, goal conditioned policy that can be
easily transferred from simulation to the real robot.
