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.

该论文介绍了一种针对目标导航任务的框架，其中机器人需要从随机起始位置找到并移动到最近的目标对象类的实例。该框架利用机器人轨迹的历史记录来学习空间关系图和基于图卷积网络的嵌入，以便估计不同语义标签区域的接近度以及这些区域中不同的对象类别的出现。在评估期间，机器人使用贝叶斯推断和空间关系图来估计可见区域，并使用已学习的图卷积网络嵌入来排名可见区域并选择下一个探索区域。