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Sim2Real transfer has gained popularity because it helps transfer frominexpensive simulators to real world. This paper presents a novel system thatfuses components in a traditional \textit{World Model} into a robust system,trained entirely within a simulator, that \textit{Zero-Shot} transfers to thereal world. To facilitate transfer, we use an intermediary representation thatare based on \textit{Bird's Eye View (BEV)} images. Thus, our robot learns tonavigate in a simulator by first learning to translate from complex\textit{First-Person View (FPV)} based RGB images to BEV representations, thenlearning to navigate using those representations. Later, when tested in thereal world, the robot uses the perception model that translates FPV-based RGBimages to embeddings that are used by the downstream policy. The incorporationof state-checking modules using \textit{Anchor images} and \textit{MixtureDensity LSTM} not only interpolates uncertain and missing observations but alsoenhances the robustness of the model when exposed to the real-worldenvironment. We trained the model using data collected using a\textit{Differential drive} robot in the CARLA simulator. Our methodology'seffectiveness is shown through the deployment of trained models onto a\textit{Real world Differential drive} robot. Lastly we release a comprehensivecodebase, dataset and models for training and deployment that are available tothe public.

World Model Based Sim2Real Transfer for Visual Navigation