Aug 2026· Applied Sciences· Vol 16, pp. 8050· 0 citations· 37 references
TL;DR
This survey revisits VLN from a component-internal perspective, viewing it as a navigation system composed of internal components such as instructions, environment representations, and embodied agents, and organizing existing work around the functions and interactions of these components.
Abstract
Vision-and-Language Navigation (VLN) is a representative task in embodied artificial intelligence, requiring agents to perceive, understand, and make navigation decisions in partially observable environments according to natural language instructions. As research has expanded from early discrete simulation benchmarks to continuous control, interactive clarification, open-vocabulary perception, and real-world robotic deployment, VLN has evolved from a path-following multimodal task into an important research area connecting language understanding, environment modeling, spatial reasoning, and embodied execution. Existing surveys mainly organize the literature by timeline, model paradigm, or benchmark, while paying less attention to the internal components of VLN systems and their functional coupling. In this survey, we revisit VLN from a component-internal perspective, viewing it as a navigation system composed of internal components such as instructions, environment representations, and embodied agents, and organizing existing work around the functions and interactions of these components. Specifically, we summarize task definitions, datasets, and evaluation settings, and review representative methods and technical progress in instruction understanding and action generation, instruction–environment alignment, and robot–environment interaction understanding. We further discuss key trends as VLN moves from closed benchmarks toward open-world and real-world deployment, including reasoning-enhanced planning, open-vocabulary and online semantic mapping, long-horizon memory and structured spatial representation, and sim-to-real transfer across platforms. We hope this survey provides a clearer component-level analytical framework for understanding the evolution of internal VLN capabilities and for informing future method design and embodied-system deployment.
LightNav-0 is presented, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads, and establishes compact VLMs as a unified and transferable backbone for generalist embodied navigation.
Shao-An Wang, Ao-Cheng Luo, Fei Huang et al.· 0 citations
Vision-and-Language Navigation (VLN) tasks require an agent to interpret natural language instructions and visual observations to navigate complex environments. Existing methods mostly construct topological or semantic maps and rely on the Large Language Model (LLM) for navigation decision-making, however, they still suffer from limited adaptability to dynamic environments and robustness to complex instructions, with task performance being limited by LLM performance. To overcome these limitations, we introduce the Real-time Occupancy-aware Visual Language Map (RO-VLMap). This framework quantifies the instantaneous risks posed by moving entities in the environment by fusing real-time occupancy sensing with visual language 3D reconstruction. Specifically, RO-VLMap, when combined with our proposed Navigation Adaptive Module (NAM), leverages the complementary advantages of the Knowledge Graph (KG) and LLM to parse open-vocabulary instructions into precise navigation goals. By planning over a unified occupancy-aware map, the agent proactively generates safe paths that avoid dynamic obstacles. Experimental results show that RO-VLMap significantly improves navigation success rates and efficiency. Furthermore, it demonstrates strong robustness in unseen scenarios, providing a practical solution for autonomous navigation of embodied agents in complex real-world environments.
Yuan Liu, Chuang Hu, Nan Ding· International Conferences on...· 0 citations
Vision-and-Language Navigation in continuous environments (VLN-CE) requires an agent to ground language in egocentric observations and plan in unseen scenes. Although recent multimodal large models and world-model-based methods have improved navigation, they often preserve excessive task-irrelevant detail, weakening generalization and increasing computational burden. We propose BrainNav, a navigation framework grounded in the Principle of Minimal Sufficiency. BrainNav consists of three components: a Logical Anchor Model that implements instruction-aware selective perception to suppress environmental noise, a Minimalist Constraint Alignment module that serves as a compact cross-modal bottleneck, efficiently synchronizing discrete linguistic intent with continuous latent dynamics while filtering out redundant information, and a Compression World Model that predicts action-conditioned states within a condensed, low-rank latent space. These modules align semantic intent with spatial perception, enhancing the agent's robustness and efficiency in complex tasks. Experiments show that BrainNav improves over prior SOTA by 2.0 % / 1.0 in SR/SPL on R2R-CE val-unseen and 0.94 % / 0.78 on RxR-CE val-unseen. These results indicate that minimally sufficient world representations provide an effective foundation for robust VLN.
Yihao Wu, Chen-Yi Xu, Li-Qi Yan et al.· arXiv.org· 0 citations
For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those actions. Rapid progress in the domains of representation learning, VLA models, and world models has significantly enhanced the capabilities of robot learning systems, enabling robots to work in increasingly complex environments. However, these paradigms are typically developed in isolation, resulting in fragmented systems that struggle with generalization, long-horizon temporal reasoning and planning, and deployment in unstructured environments. In this survey, we present a unified perspective on robot learning by organizing the existing methods along three complementary axes: understanding through representation learning, acting through VLA models, and reasoning through world models. We introduce a structured taxonomy that captures key design choices in environment representation, policy learning, and predictive modeling, and summarize the recent progress in these domains. Beyond classifying the existing works, we analyze how these components interact, discuss common limitations, and highlight emerging trends towards more integrated systems. Through this lens, we identify the challenges in the domain of robot learning, including uncertainty quantification, out-of-distribution generalization, cross-embodiment transfer, long-context understanding, and long-horizon planning. We argue that these challenges arise not only from limitations within individual components but also from the lack of integration across perception, action, and reasoning. Building on this analysis, we outline future directions towards unified, physically grounded, and probabilistic robot learning to develop robust robotic systems that maintain consistent internal representations and support decision making over extended interactions in real-world environments.
Shaunak A. Mehta, Ananya Hazarika, Hao-Chen Zhang et al.· Trans. Mach. Learn. Res.· 0 citations
The World-Cognition Model is presented, a human-centered embodied agent built on the SLAK architecture (Sensing, Logic, Action, and Knowledge) and an asynchronous runtime and introduces a human-in-the-loop teaching mode that enables users to interactively teach the robot difficult or long-horizon tasks.
A persistent map's memory yields the best schedule (held-out), matching an oracle, while the memoryless VLM prior is poor, showing a persistent map's memory earns its keep not by telling the robot how to walk around a room, but by telling it what to pay attention to.
Dibyendu Ghosh· arXiv.org· 0 citations
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