[Submitted on 9 Jul 2026]
Title:A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation
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Abstract:Navigation is a fundamental capability of autonomous systems, yet most existing approaches rely on highly structured models and strong prior assumptions, limiting their robustness in open and uncertain real-world environments. Vision-and-Language Navigation (VLN) offers a promising direction by enabling robots to integrate natural language understanding with visual perception in a data-driven manner. Although VLN has attracted increasing research attention, systematic methodological taxonomy and real-world validation remain limited. This survey presents a comprehensive review of VLN research. Specifically, state-of-the-art methods are organized along two orthogonal dimensions: action paradigms, including hierarchical and monolithic frameworks, and model paradigms, including discriminative and generative approaches. A critical analysis of their respective strengths and limitations is provided. Additionally, we conduct a systematic real-world evaluation of representative VLN system configurations on a physical robotic platform. Experiments across ten diverse real-world scenes show a substantial performance gap between simulation and real-world deployment under the tested configurations: a representative monolithic RGB-only method achieves 61% success in simulation but drops to 22% in real-world deployment, while a hierarchical framework achieves a higher real-world success rate of 51%, suggesting stronger robustness in our evaluation setting. Finally, we highlight key challenges in perception, decision-making, and control that must be addressed in future research.
Comments: This paper has been accepted by IEEE TASE 2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.09792 [cs.RO]
(or arXiv:2607.09792v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.09792
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Liuyi Wang [view email] [v1] Thu, 9 Jul 2026 05:13:02 UTC (16,119 KB)
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