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.

翻译：鉴于基因对抗网络(GANs)的多功能性,我们力求了解利用现有GAN加强模拟图像和减少模拟图像与现实差距的好处;我们在模拟机器人性能和图像感知方面进行分析;具体地说,我们量化GAN在机器人图像感知方面减少模拟与现实差异的能力;使用语义分解,我们利用城市环境的名义和强化模拟,分析培训和测试方面的模拟与实际差异。作为次要应用,我们考虑利用GAN加强室内环境。为此,利用物体探测分析培训和测试的改进情况。结果显示在使用GAN时减少模拟与现实差距的情况,并展示其使用的好处。