Work

A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation

Year: 2026

Type: article

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. Note to Practitioners—For practitioners building mobile robots that navigate using natural language commands, this survey provides practical guidance on selecting, designing, and deploying VLN systems. Our empirical evaluations in real-world environments indicate that architectural choices have a substantial impact on both navigation performance and operational safety. At present, hierarchical navigation systems that combine panoramic visual perception with explicit 3D sensing and decompose navigation into high-level planning and low-level control achieve more reliable task success and lower collision frequencies. At the same time, several practical limitations remain. Current VLN systems often struggle to determine when to stop based on the semantic intent of an instruction rather than simple distance-based heuristics. They also show limited robustness when faced with ambiguous or underspecified instructions that commonly arise in complex human-robot interactions. Continued advances in intent understanding, safety-aware reasoning, and real-world learning are expected to gradually enable more flexible and robust deployment in less structured settings.

Source: IEEE Transactions on Automation Science and Engineering

DOI: 10.1109/tase.2026.3711284