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.

中文翻译：

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使用面向目标的语义探索的对象目标导航

这项工作研究了对象目标导航的问题，该问题涉及在看不见的环境中导航到给定对象类别的实例。基于端到端学习的导航方法难以完成这项任务，因为它们在探索和长期规划方面效率低下。我们提出了一个名为“面向目标的语义探索”的模块化系统，它构建了一个情节语义地图，并使用它来基于目标对象类别有效地探索环境。在视觉逼真的模拟环境中的实证结果表明，所提出的模型优于广泛的基线，包括基于端到端学习的方法以及基于模块化地图的方法，并赢得了 CVPR-2020 Habitat ObjectNav Challenge 的参赛资格. 消融分析表明，所提出的模型学习场景中对象相对排列的语义先验，并使用它们进行有效探索。领域不可知的模块设计允许我们将我们的模型转移到移动机器人平台，并在现实世界中实现类似的目标导航性能。