  In Bansal et al. (2019), a novel visual navigation framework that combines
learning-based and model-based approaches has been proposed. Specifically, a
Convolutional Neural Network (CNN) predicts a waypoint that is used by the
dynamics model for planning and tracking a trajectory to the waypoint. However,
the CNN inevitably makes prediction errors which often lead to collisions in
cluttered and tight spaces. In this paper, we present a novel Hamilton-Jacobi
(HJ) reachability-based method to generate supervision for the CNN for waypoint
prediction in an unseen environment. By modeling CNN prediction error as
"disturbances" in robot's dynamics, our generated waypoints are robust to these
disturbances, and consequently to the prediction errors. Moreover, using
globally optimal HJ reachability analysis leads to predicting waypoints that
are time-efficient and avoid greedy behavior. Through simulations and hardware
experiments, we demonstrate the advantages of the proposed approach on
navigating through cluttered, narrow indoor environments.
