Efficient robotic grasping using simulation and domain adaptation.

Data collection for training robotic grasping controllers is expensive in both time and price. Methods for making use of simulated data are very appealing as they reduce this expense dramatically, but often fail to generalise to a real world environment. GraspGAN is a application of pixel level domain adaptation that can generate synthetic training data good enough that we can reduce the amount of real world data required by a factor of 50.