  Real world visual navigation requires robots to operate in unfamiliar,
human-occupied dynamic environments. Navigation around humans is especially
difficult because it requires anticipating their future motion, which can be
quite challenging. We propose an approach that combines learning-based
perception with model-based optimal control to navigate among humans based only
on monocular, first-person RGB images. Our approach is enabled by our novel
data-generation tool, HumANav that allows for photorealistic renderings of
indoor environment scenes with humans in them, which are then used to train the
perception module entirely in simulation. Through simulations and experiments
on a mobile robot, we demonstrate that the learned navigation policies can
anticipate and react to humans without explicitly predicting future human
motion, generalize to previously unseen environments and human behaviors, and
transfer directly from simulation to reality. Videos describing our approach
and experiments, as well as a demo of HumANav are available on the project
website.
