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.
@article{arxiv.2203.12667,
title = {Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions},
author = {Jing Gu and Eliana Stefani and Qi Wu and Jesse Thomason and Xin Eric Wang},
journal= {arXiv preprint arXiv:2203.12667},
year = {2022}
}