  Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples.
