This work studies the problem of object goal navigation which involves
navigating to an instance of the given object category in unseen environments.
End-to-end learning-based navigation methods struggle at this task as they are
ineffective at exploration and long-term planning. We propose a modular system
called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic
map and uses it to explore the environment efficiently based on the goal object
category. Empirical results in visually realistic simulation environments show
that the proposed model outperforms a wide range of baselines including
end-to-end learning-based methods as well as modular map-based methods and led
to the winning entry of the CVPR-2020 Habitat ObjectNav Challenge. Ablation
analysis indicates that the proposed model learns semantic priors of the
relative arrangement of objects in a scene, and uses them to explore
efficiently. Domain-agnostic module design allow us to transfer our model to a
mobile robot platform and achieve similar performance for object goal
navigation in the real-world.

该研究提出了 Goal-Oriented Semantic Exploration 系统，通过构建基于目标对象类别的情节语义地图来有效探索环境，该模型在模拟环境和移动机器人平台中实现了物体目标导航的优异表现。