Learning to Plan for Visibility in Navigation of Unknown Environments

For robots navigating in unknown environments, naively following the shortest path toward the goal often leads to poor visibility of free space, limiting navigation speed, or even preventing forward progress altogether. In this work, we train a guidance function to give the robot greater visibility into unknown parts of the environment. Unlike exploration techniques that aim to observe as much map as possible for its own sake, we reason about the value of future observations directly in terms of expected cost-to-goal. We show significant improvements in navigation speed and success rate for narrow field-of-view sensors such as popular RGBD cameras. However, contrary to our expectations, we show that our strategy makes little difference for sensors with fields-of-view greater than 80\(^{\circ }\), and we discuss why the naive strategy is hard to beat.