  Recent work has presented embodied agents that can navigate to point-goal
targets in novel indoor environments with near-perfect accuracy. However, these
agents are equipped with idealized sensors for localization and take
deterministic actions. This setting is practically sterile by comparison to the
dirty reality of noisy sensors and actuations in the real world -- wheels can
slip, motion sensors have error, actuations can rebound. In this work, we take
a step towards this noisy reality, developing point-goal navigation agents that
rely on visual estimates of egomotion under noisy action dynamics. We find
these agents outperform naive adaptions of current point-goal agents to this
setting as well as those incorporating classic localization baselines. Further,
our model conceptually divides learning agent dynamics or odometry (where am
I?) from task-specific navigation policy (where do I want to go?). This enables
a seamless adaption to changing dynamics (a different robot or floor type) by
simply re-calibrating the visual odometry model -- circumventing the expense of
re-training of the navigation policy. Our agent was the runner-up in the
PointNav track of CVPR 2020 Habitat Challenge.
