  Long-range indoor navigation requires guiding robots with noisy sensors and
controls through cluttered environments along paths that span a variety of
buildings. We achieve this with PRM-RL, a hierarchical robot navigation method
in which reinforcement learning agents that map noisy sensors to robot controls
learn to solve short-range obstacle avoidance tasks, and then sampling-based
planners map where these agents can reliably navigate in simulation; these
roadmaps and agents are then deployed on robots, guiding them along the
shortest path where the agents are likely to succeed. Here we use Probabilistic
Roadmaps (PRMs) as the sampling-based planner, and AutoRL as the reinforcement
learning method in the indoor navigation context. We evaluate the method in
simulation for kinematic differential drive and kinodynamic car-like robots in
several environments, and on differential-drive robots at three physical sites.
Our results show PRM-RL with AutoRL is more successful than several baselines,
is robust to noise, and can guide robots over hundreds of meters in the face of
noise and obstacles in both simulation and on robots, including over 5.8
kilometers of physical robot navigation. Video: https://youtu.be/xN-OWX5gKvQ
