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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 World Model into a robust system, trainedentirely within a simulator, that Zero-Shot transfers to the real world. Tofacilitate transfer, we use an intermediary representation that is based on\textit{Bird's Eye View (BEV)} images. Thus, our robot learns to navigate in asimulator by first learning to translate from complex \textit{First-Person View(FPV)} based RGB images to BEV representations, then learning to navigate usingthose representations. Later, when tested in the real world, the robot uses theperception model that translates FPV-based RGB images to embeddings that werelearned by the FPV to BEV translator and that can be used by the downstreampolicy. The incorporation of state-checking modules using \textit{Anchorimages} and Mixture Density LSTM not only interpolates uncertain and missingobservations but also enhances the robustness of the model in the real-world.We trained the model using data from a Differential drive robot in the CARLAsimulator. Our methodology's effectiveness is shown through the deployment oftrained models onto a real-world Differential drive robot. Lastly we release acomprehensive codebase, dataset and models for training and deployment(\url{https://sites.google.com/view/value-explicit-pretraining}).

Bird's Eye View Based Pretrained World model for Visual Navigation