  Even though the peg-hole insertion is one of the well-studied problems in
robotics, it still remains a challenge for robots, especially when it comes to
flexibility and the ability to generalize. Successful completion of the task
requires combining several modalities to cope with the complexity of the real
world. In our work, we focus on the visual aspect of the problem and employ the
strategy of learning an insertion task in a simulator. We use Deep
Reinforcement Learning to learn the policy end-to-end and then transfer the
learned model to the real robot, without any additional fine-tuning. We show
that the transferred policy, which only takes RGB-D and joint information
(proprioception) can perform well on the real robot.
