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Real world visual navigation requires robots to operate in unfamiliar,human-occupied dynamic environments. Navigation around humans is especiallydifficult because it requires anticipating their future motion, which can bequite challenging. We propose an approach that combines learning-basedperception with model-based optimal control to navigate among humans based onlyon monocular, first-person RGB images. Our approach is enabled by our noveldata-generation tool, HumANav that allows for photorealistic renderings ofindoor environment scenes with humans in them, which are then used to train theperception module entirely in simulation. Through simulations and experimentson a mobile robot, we demonstrate that the learned navigation policies cananticipate and react to humans without explicitly predicting future humanmotion, generalize to previously unseen environments and human behaviors, andtransfer directly from simulation to reality. Videos describing our approachand experiments, as well as a demo of HumANav are available on the projectwebsite.

Visual Navigation Among Humans with Optimal Control as a Supervisor