Navigation is one of the fundamental features of a autonomous robot. And the ability of long-term navigation with semantic instruction is a `holy grail` goals of intelligent robots. The development of 3D simulation technology provide a large scale of data to simulate the real-world environment. The deep learning proves its ability to robustly learn various embodied navigation tasks. However, deep learning on embodied navigation is still in its infancy due to the unique challenges faced by the navigation exploration and learning from partial observed visual input. Recently, deep learning in embodied navigation has become even thriving, with numerous methods have been proposed to tackle different challenges in this area. To give a promising direction for future research, in this paper, we present a comprehensive review of embodied navigation tasks and the recent progress in deep learning based methods. It includes two major tasks: target-oriented navigation and the instruction-oriented navigation.

翻译：3D模拟技术的开发为模拟真实世界环境提供了大范围的数据。深层次的学习证明它有能力强有力地学习各种包含的导航任务。然而,由于导航探索和部分观测到的视觉输入所面临的独特挑战,关于体现的导航的深层次学习仍然处于萌芽阶段。最近,在体现的导航中深层的学习变得甚至更加蓬勃,提出了应对该领域不同挑战的多种方法。为了给未来研究提供有希望的方向,我们在本文件中对包含的导航任务和最近在深层次学习方法方面取得的进展进行全面审查,其中包括两项主要任务:面向目标的导航和面向指示的导航。