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

TL;DR

This paper explores augmenting reactive robot navigation with neural networks to improve the ability to escape local minima, demonstrating successful transfer from simulation to real environments and robustness to sensor noise.

Contribution

It introduces a neural network-based approach that combines reactive planning with learned geometric intuition, bridging the gap between reactive and map-based navigation.

Findings

Zero-shot transfer to real 3D environments

Handles up to 30% sensor noise without performance loss

Neural augmentation improves escape from local minima

Abstract

When does a robot planner need a 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 fully reactive methods and map-based path planners? In this paper, we investigate feed forward and recurrent networks to augment a purely reactive sensor-based planner, which should give the robot geometric intuition about how to escape local minima. We train on a large number of extremely cluttered worlds auto-generated from primitive shapes, and show that our system zero-shot transfers to real 3D man-made environments, and can handle up to 30% sensor noise without degeneration 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.