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We propose SplitNet, a method for decoupling visual perception and policylearning. By incorporating auxiliary tasks and selective learning of portionsof the model, we explicitly decompose the learning objectives for visualnavigation into perceiving the world and acting on that perception. We showdramatic improvements over baseline models on transferring between simulators,an encouraging step towards Sim2Real. Additionally, SplitNet generalizes betterto unseen environments from the same simulator and transfers faster and moreeffectively to novel embodied navigation tasks. Further, given only a smallsample from a target domain, SplitNet can match the performance of traditionalend-to-end pipelines which receive the entire dataset. Code is availablehttps://github.com/facebookresearch/splitnet

SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation