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

Sim2Real for Peg-Hole Insertion with Eye-in-Hand Camera