# LangNav: Language as a Perceptual Representation for Navigation

arXiv: https://arxiv.org/abs/2310.07889 · HTML: https://mlsanity.com/paper/2310.07889/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 67 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 30 of 59)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Speaker-Follower Models for Vision-and-Language Navigation](https://mlsanity.com/paper/1806.02724/) (2018)
- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://mlsanity.com/paper/1811.10092/) (2018)
- [A Recurrent Vision-and-Language BERT for Navigation](https://mlsanity.com/paper/2011.13922/) (2020)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [History Aware Multimodal Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.13309/) (2021)
- [The False Promise of Imitating Proprietary LLMs](https://mlsanity.com/paper/2305.15717/) (2023)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)

## Built on by (most cited first, 30 of 47)

- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models](https://mlsanity.com/paper/2407.12366/) (2024)
- [MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation](https://mlsanity.com/paper/2401.07314/) (2024)
- [Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models](https://mlsanity.com/paper/2407.07035/) (2024)
- [Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation](https://mlsanity.com/paper/2407.05890/) (2024)
- [Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks](https://mlsanity.com/paper/2503.21696/) (2025)
- [SmartWay: Enhanced Waypoint Prediction and Backtracking for Zero-Shot Vision-and-Language Navigation](https://mlsanity.com/paper/2503.10069/) (2025)
- [RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation](https://mlsanity.com/paper/2412.08591/) (2024)
- [FlexVLN: Flexible Adaptation for Diverse Vision-and-Language Navigation Tasks](https://mlsanity.com/paper/2503.13966/) (2025)
- [Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation](https://mlsanity.com/paper/2505.20897/) (2025)
- [SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts](https://mlsanity.com/paper/2412.05552/) (2024)
- [VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation](https://mlsanity.com/paper/2403.12415/) (2024)
- [LookOut: Real-World Humanoid Egocentric Navigation](https://mlsanity.com/paper/2508.14466/) (2025)
- [CitySeeker: How Do VLMS Explore Embodied Urban Navigation With Implicit Human Needs?](https://mlsanity.com/paper/2512.16755/) (2025)
- [TINA: Think, Interaction, and Action Framework for Zero-Shot Vision Language Navigation](https://mlsanity.com/paper/2403.08833/) (2024)
- [LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments](https://mlsanity.com/paper/2406.16294/) (2024)
- [Evaluating Vision-Language Models as Evaluators in Path Planning](https://mlsanity.com/paper/2411.18711/) (2024)
- [NavQ: Learning a Q-Model for Foresighted Vision-and-Language Navigation](https://mlsanity.com/paper/2510.16457/) (2025)
- [DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes](https://mlsanity.com/paper/2410.02730/) (2024)
- [Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach](https://mlsanity.com/paper/2502.00114/) (2025)
- [VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents](https://mlsanity.com/paper/2512.24851/) (2025)
- [Trajectory-Diversity-Driven Robust Vision-and-Language Navigation](https://mlsanity.com/paper/2603.15370/) (2026)
- [Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation](https://mlsanity.com/paper/2506.15757/) (2025)
- [MSNav: Zero-Shot Vision-and-Language Navigation with Dynamic Memory and LLM Spatial Reasoning](https://mlsanity.com/paper/2508.16654/) (2025)
- [Boosting Zero-Shot VLN via Abstract Obstacle Map-Based Waypoint Prediction with TopoGraph-and-VisitInfo-Aware Prompting](https://mlsanity.com/paper/2509.20499/) (2025)
- [Vision-and-Language Navigation with Analogical Textual Descriptions in LLMs](https://mlsanity.com/paper/2509.25139/) (2025)
- [Embodied Navigation with Auxiliary Task of Action Description Prediction](https://mlsanity.com/paper/2510.21809/) (2025)
- [UNeMo: Collaborative Visual-Language Reasoning and Navigation via a Multimodal World Model](https://mlsanity.com/paper/2511.18845/) (2025)
- [Stop Wandering: Efficient Vision-Language Navigation via Metacognitive Reasoning](https://mlsanity.com/paper/2604.02318/) (2026)
- [VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions](https://mlsanity.com/paper/2604.10533/) (2026)

## Abstract (arXiv)

We explore the use of language as a perceptual representation for vision-and-language navigation (VLN), with a focus on low-data settings. Our approach uses off-the-shelf vision systems for image captioning and object detection to convert an agent's egocentric panoramic view at each time step into natural language descriptions. We then finetune a pretrained language model to select an action, based on the current view and the trajectory history, that would best fulfill the navigation instructions. In contrast to the standard setup which adapts a pretrained language model to work directly with continuous visual features from pretrained vision models, our approach instead uses (discrete) language as the perceptual representation. We explore several use cases of our language-based navigation (LangNav) approach on the R2R VLN benchmark: generating synthetic trajectories from a prompted language model (GPT-4) with which to finetune a smaller language model; domain transfer where we transfer a policy learned on one simulated environment (ALFRED) to another (more realistic) environment (R2R); and combining both vision- and language-based representations for VLN. Our approach is found to improve upon baselines that rely on visual features in settings where only a few expert trajectories (10-100) are available, demonstrating the potential of language as a perceptual representation for navigation.