  We present a robot navigation system that uses an imitation learning
framework to successfully navigate in complex environments. Our framework takes
a pre-built 3D scan of a real environment and trains an agent from
pre-generated expert trajectories to navigate to any position given a panoramic
view of the goal and the current visual input without relying on map, compass,
odometry, or relative position of the target at runtime. Our end-to-end trained
agent uses RGB and depth (RGBD) information and can handle large environments
(up to $1031m^2$) across multiple rooms (up to $40$) and generalizes to unseen
targets. We show that when compared to several baselines our method (1)
requires fewer training examples and less training time, (2) reaches the goal
location with higher accuracy, and (3) produces better solutions with shorter
paths for long-range navigation tasks.
