  A long-term goal of AI research is to build intelligent agents that can
communicate with humans in natural language, perceive the environment, and
perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundamental
and interdisciplinary research topic towards this goal, and receives increasing
attention from natural language processing, computer vision, robotics, and
machine learning communities. In this paper, we review contemporary studies in
the emerging field of VLN, covering tasks, evaluation metrics, methods, etc.
Through structured analysis of current progress and challenges, we highlight
the limitations of current VLN and opportunities for future work. This paper
serves as a thorough reference for the VLN research community.
