Pushing the Limits of Reactive Navigation: Learning to Escape Local Minima

Can a robot navigate a cluttered environment without an explicit map? Reactive methods that use only the robot's current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between reactive methods and map-based path planners? In this paper, we investigate feed forward and recurrent networks to augment a purely reactive sensor-based navigation algorithm, which should give the robot “geometric intuition” about how to escape local minima. We train on a large number of extremely cluttered simulated worlds, auto-generated from primitive shapes, and show that our system zero-shot transfers to worlds based on real data 3D man-made environments, and can handle up to 30% sensor noise without degradation of performance. We also offer a discussion of what role network memory plays in our final system, and what insights can be drawn about the nature of reactive vs. map-based navigation.