  Sim2Real transfer has gained popularity because it helps transfer from
inexpensive simulators to real world. This paper presents a novel system that
fuses components in a traditional World Model into a robust system, trained
entirely within a simulator, that Zero-Shot transfers to the real world. To
facilitate transfer, we use an intermediary representation that is based on
\textit{Bird's Eye View (BEV)} images. Thus, our robot learns to navigate in a
simulator by first learning to translate from complex \textit{First-Person View
(FPV)} based RGB images to BEV representations, then learning to navigate using
those representations. Later, when tested in the real world, the robot uses the
perception model that translates FPV-based RGB images to embeddings that were
learned by the FPV to BEV translator and that can be used by the downstream
policy. The incorporation of state-checking modules using \textit{Anchor
images} and Mixture Density LSTM not only interpolates uncertain and missing
observations 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 CARLA
simulator. Our methodology's effectiveness is shown through the deployment of
trained models onto a real-world Differential drive robot. Lastly we release a
comprehensive codebase, dataset and models for training and deployment
(\url{https://sites.google.com/view/value-explicit-pretraining}).
