Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions
Jing Gu, Eliana Stefani, Qi Wu, Jesse Thomason, Xin Wang
Abstract
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. Visionand-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. 1
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Cited by top-tier papers46
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- MapNav: A Novel Memory Representation via Annotated Semantic Maps for VLM-based Vision-and-Language NavigationLingfeng Zhang, Xiaoshuai Hao, Qinwen Xu, Qiang Zhang et al.ACL 2025 · 55 citations
- OmniNav: A Unified Framework for Prospective Exploration and Visual-Language NavigationXinda Xue, Junjun Hu, Minghua Luo, Xie Shichao et al.ICLR 2026 · 51 citations
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- DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion FramesErik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee et al.ICLR 2020 · 608 citations
- Episodic Transformer for Vision-and-Language NavigationAlexander Pashevich, Cordelia Schmid, Chen SunICCV 2021 · 228 citations
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