  The rise of deep learning has caused a paradigm shift in robotics research,
favoring methods that require large amounts of data. Unfortunately, it is
prohibitively expensive to generate such data sets on a physical platform.
Therefore, state-of-the-art approaches learn in simulation where data
generation is fast as well as inexpensive and subsequently transfer the
knowledge to the real robot (sim-to-real). Despite becoming increasingly
realistic, all simulators are by construction based on models, hence inevitably
imperfect. This raises the question of how simulators can be modified to
facilitate learning robot control policies and overcome the mismatch between
simulation and reality, often called the 'reality gap'. We provide a
comprehensive review of sim-to-real research for robotics, focusing on a
technique named 'domain randomization' which is a method for learning from
randomized simulations.
