There has been significant research in solving manipulation tasks involving many research/collaborative robots. The objective of this work is to augment the capability of industrial robots by teaching them new tricks. The means of achieving this is by bridging the gap between industrial robots and state-of-the-art reinforcement learning-based controllers. Specifically, to learn controllers to solve non-prehensile manipulation tasks in pybullet and transfer them to the KUKA-KR5-Arc robot. The deployed hierarchical control architecture allows for the successful transfer of the simulated agent.