  Recently, deep reinforcement learning (RL) has shown some impressive
successes in robotic manipulation applications. However, training robots in the
real world is nontrivial owing to sample efficiency and safety concerns.
Sim-to-real transfer is proposed to address the aforementioned concerns but
introduces a new issue called the reality gap. In this work, we introduce a
sim-to-real learning framework for vision-based assembly tasks and perform
training in a simulated environment by employing inputs from a single camera to
address the aforementioned issues. We present a domain adaptation method based
on cycle-consistent generative adversarial networks (CycleGAN) and a force
control transfer approach to bridge the reality gap. We demonstrate that the
proposed framework trained in a simulated environment can be successfully
transferred to a real peg-in-hole setup.
