A Behavioral Approach to Visual Navigation with Graph Localization Networks

Abstract

Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual observations and the topological map of the environment. To this end, we propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator and the Stanford 2D-3D-S dataset, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen indoor environments.