  In a typical path planning pipeline for a ground robot, we build a map (e.g.,
an occupancy grid) of the environment as the robot moves around. While
navigating indoors, a ground robot's knowledge about the environment may be
limited due to occlusions. Therefore, the map will have many as-yet-unknown
regions that may need to be avoided by a conservative planner. Instead, if a
robot is able to correctly predict what its surroundings and occluded regions
look like, the robot may be more efficient in navigation. In this work, we
focus on predicting occupancy within the reachable distance of the robot to
enable faster navigation and present a self-supervised proximity occupancy map
prediction method, named ProxMaP. We show that ProxMaP generalizes well across
realistic and real domains, and improves the robot navigation efficiency in
simulation by \textbf{$12.40\%$} against the traditional navigation method. We
share our findings on our project webpage (see
https://raaslab.org/projects/ProxMaP ).
