  Given the versatility of generative adversarial networks (GANs), we seek to
understand the benefits gained from using an existing GAN to enhance simulated
images and reduce the sim-to-real gap. We conduct an analysis in the context of
simulating robot performance and image-based perception. Specifically, we
quantify the GAN's ability to reduce the sim-to-real difference in image
perception in robotics. Using semantic segmentation, we analyze the sim-to-real
difference in training and testing, using nominal and enhanced simulation of a
city environment. As a secondary application, we consider use of the GAN in
enhancing an indoor environment. For this application, object detection is used
to analyze the enhancement in training and testing. The results presented
quantify the reduction in the sim-to-real gap when using the GAN, and
illustrate the benefits of its use.
