# Learning Vision-and-Language Navigation from YouTube Videos

arXiv: https://arxiv.org/abs/2307.11984 · HTML: https://mlsanity.com/paper/2307.11984/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 63 references that are in the map.
- First posted on arXiv 22 Jul 2023.

## Builds on (most cited first, 30 of 54)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Efficient Test-Time Model Adaptation without Forgetting](https://mlsanity.com/paper/2204.02610/) (2022)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [Speaker-Follower Models for Vision-and-Language Navigation](https://mlsanity.com/paper/1806.02724/) (2018)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [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)
- [History Aware Multimodal Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.13309/) (2021)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://mlsanity.com/paper/1904.04195/) (2019)
- [Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training](https://mlsanity.com/paper/2002.10638/) (2020)
- [Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2202.11742/) (2022)
- [Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks](https://mlsanity.com/paper/1911.07883/) (2019)
- [Improving Vision-and-Language Navigation with Image-Text Pairs from the Web](https://mlsanity.com/paper/2004.14973/) (2020)
- [Self-Monitoring Navigation Agent via Auxiliary Progress Estimation](https://mlsanity.com/paper/1901.03035/) (2019)
- [Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation](https://mlsanity.com/paper/1905.12255/) (2019)

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

- [NaVILA: Legged Robot Vision-Language-Action Model for Navigation](https://mlsanity.com/paper/2412.04453/) (2024)
- [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023)
- [Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models](https://mlsanity.com/paper/2407.07035/) (2024)
- [Vision-and-Language Navigation via Causal Learning](https://mlsanity.com/paper/2404.10241/) (2024)
- [Constraint-Aware Zero-Shot Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2412.10137/) (2024)
- [Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives](https://mlsanity.com/paper/2406.05615/) (2024)
- [FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation](https://mlsanity.com/paper/2310.07473/) (2023)
- [Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel](https://mlsanity.com/paper/2412.08467/) (2024)
- [Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions](https://mlsanity.com/paper/2406.19236/) (2024)
- [Vision-Language Navigation with Energy-Based Policy](https://mlsanity.com/paper/2410.14250/) (2024)
- [NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM](https://mlsanity.com/paper/2502.11142/) (2025)
- [FlexVLN: Flexible Adaptation for Diverse Vision-and-Language Navigation Tasks](https://mlsanity.com/paper/2503.13966/) (2025)
- [DOPE: Dual Object Perception-Enhancement Network for Vision-and-Language Navigation](https://mlsanity.com/paper/2505.00743/) (2025)
- [ActiveVLN: Towards Active Exploration via Multi-Turn RL in Vision-and-Language Navigation](https://mlsanity.com/paper/2509.12618/) (2025)
- [Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments](https://mlsanity.com/paper/2407.21452/) (2024)
- [Vision-Language Navigation with Embodied Intelligence: A Survey](https://mlsanity.com/paper/2402.14304/) (2024)
- [Planning from Imagination: Episodic Simulation and Episodic Memory for Vision-and-Language Navigation](https://mlsanity.com/paper/2412.01857/) (2024)
- [Unseen from Seen: Rewriting Observation-Instruction Using Foundation Models for Augmenting Vision-Language Navigation](https://mlsanity.com/paper/2503.18065/) (2025)
- [HA-VLN 2.0: An Open Benchmark and Leaderboard for Human-Aware Navigation in Discrete and Continuous Environments with Dynamic Multi-Human Interactions](https://mlsanity.com/paper/2503.14229/) (2025)
- [MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language Navigation](https://mlsanity.com/paper/2406.17960/) (2024)
- [NavQ: Learning a Q-Model for Foresighted Vision-and-Language Navigation](https://mlsanity.com/paper/2510.16457/) (2025)
- [Generating Vision-Language Navigation Instructions Incorporated Fine-Grained Alignment Annotations](https://mlsanity.com/paper/2506.08566/) (2025)
- [NavComposer: Composing Language Instructions for Navigation Trajectories through Action-Scene-Object Modularization](https://mlsanity.com/paper/2507.10894/) (2025)
- [Trajectory-Diversity-Driven Robust Vision-and-Language Navigation](https://mlsanity.com/paper/2603.15370/) (2026)
- [VPN: Visual Prompt Navigation](https://mlsanity.com/paper/2508.01766/) (2025)
- [AgriVLN: Vision-and-Language Navigation for Agricultural Robots](https://mlsanity.com/paper/2508.07406/) (2025)
- [Contrastive Vision-Language Alignment Makes Efficient Instruction Learner](https://mlsanity.com/paper/2311.17945/) (2023)
- [Image-Goal Navigation Using Refined Feature Guidance and Scene Graph Enhancement](https://mlsanity.com/paper/2503.10986/) (2025)
- [NaVIP: An Image-Centric Indoor Navigation Solution for Visually Impaired People](https://mlsanity.com/paper/2410.18109/) (2024)
- [World-Consistent Data Generation for Vision-and-Language Navigation](https://mlsanity.com/paper/2412.06413/) (2024)

## Abstract (arXiv)

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3D environments using natural language instructions. Existing VLN methods suffer from training on small-scale environments or unreasonable path-instruction datasets, limiting the generalization to unseen environments. There are massive house tour videos on YouTube, providing abundant real navigation experiences and layout information. However, these videos have not been explored for VLN before. In this paper, we propose to learn an agent from these videos by creating a large-scale dataset which comprises reasonable path-instruction pairs from house tour videos and pre-training the agent on it. To achieve this, we have to tackle the challenges of automatically constructing path-instruction pairs and exploiting real layout knowledge from raw and unlabeled videos. To address these, we first leverage an entropy-based method to construct the nodes of a path trajectory. Then, we propose an action-aware generator for generating instructions from unlabeled trajectories. Last, we devise a trajectory judgment pretext task to encourage the agent to mine the layout knowledge. Experimental results show that our method achieves state-of-the-art performance on two popular benchmarks (R2R and REVERIE). Code is available at https://github.com/JeremyLinky/YouTube-VLN