论文标题
对象目标导航使用面向目标的语义探索
Object Goal Navigation using Goal-Oriented Semantic Exploration
论文作者
论文摘要
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。基于端到端的基于学习的导航方法在此任务上遇到了困难，因为它们在探索和长期计划方面无效。我们提出了一个名为“面向目标的语义探索”的模块化系统，该系统构建了情节的语义图，并使用它根据目标对象类别有效地探索环境。视觉上逼真的模拟环境中的经验结果表明，所提出的模型的表现优于广泛的基础线，包括基于端到端的学习方法以及基于模块化的MAP方法，并导致CVPR-2020 Habitat Objectat Objectnav挑战的获胜。消融分析表明，所提出的模型学习了场景中对象相对排列的语义先验，并利用它们有效地探索。域 - 不知不线的模块设计使我们能够将模型转移到移动机器人平台上，并在现实世界中实现与对象目标导航相似的性能。
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.
