  Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an
understanding of the spatial and semantic regularities in environment layouts.
In this work, we present a straightforward method for learning these
regularities by predicting the locations of unobserved objects from incomplete
semantic maps. Our method differs from previous prediction-based navigation
methods, such as frontier potential prediction or egocentric map completion, by
directly predicting unseen targets while leveraging the global context from all
previously explored areas. Our prediction model is lightweight and can be
trained in a supervised manner using a relatively small amount of passively
collected data. Once trained, the model can be incorporated into a modular
pipeline for ObjectNav without the need for any reinforcement learning. We
validate the effectiveness of our method on the HM3D and MP3D ObjectNav
datasets. We find that it achieves the state-of-the-art on both datasets,
despite not using any additional data for training.
