  Vision and learning have made significant progress that could improve
robotics policies for complex tasks and environments. Learning deep neural
networks for image understanding, however, requires large amounts of
domain-specific visual data. While collecting such data from real robots is
possible, such an approach limits the scalability as learning policies
typically requires thousands of trials. In this work we attempt to learn
manipulation policies in simulated environments. Simulators enable scalability
and provide access to the underlying world state during training. Policies
learned in simulators, however, do not transfer well to real scenes given the
domain gap between real and synthetic data. We follow recent work on domain
randomization and augment synthetic images with sequences of random
transformations. Our main contribution is to optimize the augmentation strategy
for sim2real transfer and to enable domain-independent policy learning. We
design an efficient search for depth image augmentations using object
localization as a proxy task. Given the resulting sequence of random
transformations, we use it to augment synthetic depth images during policy
learning. Our augmentation strategy is policy-independent and enables policy
learning with no real images. We demonstrate our approach to significantly
improve accuracy on three manipulation tasks evaluated on a real robot.
