  Learning robot tasks or controllers using deep reinforcement learning has
been proven effective in simulations. Learning in simulation has several
advantages. For example, one can fully control the simulated environment,
including halting motions while performing computations. Another advantage when
robots are involved, is that the amount of time a robot is occupied learning a
task---rather than being productive---can be reduced by transferring the
learned task to the real robot. Transfer learning requires some amount of
fine-tuning on the real robot. For tasks which involve complex (non-linear)
dynamics, the fine-tuning itself may take a substantial amount of time. In
order to reduce the amount of fine-tuning we propose to learn robustified
controllers in simulation. Robustified controllers are learned by exploiting
the ability to change simulation parameters (both appearance and dynamics) for
successive training episodes. An additional benefit for this approach is that
it alleviates the precise determination of physics parameters for the
simulator, which is a non-trivial task. We demonstrate our proposed approach on
a real setup in which a robot aims to solve a maze game, which involves complex
dynamics due to static friction and potentially large accelerations. We show
that the amount of fine-tuning in transfer learning for a robustified
controller is substantially reduced compared to a non-robustified controller.
