  Mobile robot navigation is typically regarded as a geometric problem, in
which the robot's objective is to perceive the geometry of the environment in
order to plan collision-free paths towards a desired goal. However, a purely
geometric view of the world can can be insufficient for many navigation
problems. For example, a robot navigating based on geometry may avoid a field
of tall grass because it believes it is untraversable, and will therefore fail
to reach its desired goal. In this work, we investigate how to move beyond
these purely geometric-based approaches using a method that learns about
physical navigational affordances from experience. Our approach, which we call
BADGR, is an end-to-end learning-based mobile robot navigation system that can
be trained with self-supervised off-policy data gathered in real-world
environments, without any simulation or human supervision. BADGR can navigate
in real-world urban and off-road environments with geometrically distracting
obstacles. It can also incorporate terrain preferences, generalize to novel
environments, and continue to improve autonomously by gathering more data.
Videos, code, and other supplemental material are available on our website
https://sites.google.com/view/badgr
