  This paper describes a framework for the object-goal navigation task, which
requires a robot to find and move to the closest instance of a target object
class from a random starting position. The framework uses a history of robot
trajectories to learn a Spatial Relational Graph (SRG) and Graph Convolutional
Network (GCN)-based embeddings for the likelihood of proximity of different
semantically-labeled regions and the occurrence of different object classes in
these regions. To locate a target object instance during evaluation, the robot
uses Bayesian inference and the SRG to estimate the visible regions, and uses
the learned GCN embeddings to rank visible regions and select the region to
explore next.
