  As an attempt towards assessing the robustness of embodied navigation agents,
we propose RobustNav, a framework to quantify the performance of embodied
navigation agents when exposed to a wide variety of visual - affecting RGB
inputs - and dynamics - affecting transition dynamics - corruptions. Most
recent efforts in visual navigation have typically focused on generalizing to
novel target environments with similar appearance and dynamics characteristics.
With RobustNav, we find that some standard embodied navigation agents
significantly underperform (or fail) in the presence of visual or dynamics
corruptions. We systematically analyze the kind of idiosyncrasies that emerge
in the behavior of such agents when operating under corruptions. Finally, for
visual corruptions in RobustNav, we show that while standard techniques to
improve robustness such as data-augmentation and self-supervised adaptation
offer some zero-shot resistance and improvements in navigation performance,
there is still a long way to go in terms of recovering lost performance
relative to clean "non-corrupt" settings, warranting more research in this
direction. Our code is available at https://github.com/allenai/robustnav
