# AirNav: A Large-Scale UAV Vision-and-Language Navigation Dataset with Natural and Diverse Instructions

arXiv: https://arxiv.org/abs/2601.03707 · HTML: https://mlsanity.com/paper/2601.03707/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks](https://mlsanity.com/paper/2412.06224/) (2024)
- [AerialVLN: Vision-and-Language Navigation for UAVs](https://mlsanity.com/paper/2308.06735/) (2023)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction](https://mlsanity.com/paper/1809.00786/) (2018)
- [Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology](https://mlsanity.com/paper/2410.07087/) (2024)
- [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://mlsanity.com/paper/2201.04494/) (2022)
- [Openfly: A comprehensive platform for aerial vision-language navigation](https://mlsanity.com/paper/2502.18041/) (2025)
- [CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data](https://mlsanity.com/paper/2310.18773/) (2023)
- [Generating Content for HDR Deghosting from Frequency View](https://mlsanity.com/paper/2404.00849/) (2024)

## Built on by (most cited first, 7 of 7)

- [Fly0: Persistent Metric Anchoring for Zero-Shot Aerial Vision-Language Navigation](https://mlsanity.com/paper/2602.15875/) (2026)
- [ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue](https://mlsanity.com/paper/2605.01371/) (2026)
- [α3-SecBench: A Large-Scale Evaluation Suite of Security, Resilience, and Trust for LLM-based UAV Agents over 6G Networks](https://mlsanity.com/paper/2601.18754/) (2026)
- [CoNav-UAV: Cooperative Dual-Altitude Aerial Navigation via Stackelberg Learning](https://mlsanity.com/paper/2608.01802/) (2026)
- [DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation](https://mlsanity.com/paper/2608.12308/) (2026)
- [SatNav: A Scalable Benchmark for Long-Horizon UAV Vision-Language Navigation from Satellite Imagery](https://mlsanity.com/paper/2609.31507/) (2026)
- [WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses](https://mlsanity.com/paper/2610.08526/) (2026)

## Abstract (arXiv)

Existing UAV vision-and-language navigation (VLN) benchmarks rarely provide realistic aerial scenes, natural process-level instructions, and sufficient scale simultaneously, making it difficult to systematically train and evaluate UAV VLN agents under realistic settings. To address this, we propose AirNav, a large-scale benchmark built on real urban aerial data, comprising 137K navigation samples with natural and diverse instructions generated via a human--LLM collaborative pipeline with 10 user personas. We conduct a systematic evaluation of representative approaches on AirNav, ranging from traditional models to multimodal large language models (MLLMs), under unified metrics with open-source implementations. We further propose AirVLN-R1, trained via supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT), achieving state-of-the-art performance with a 51.82% success rate on the test-unseen split. Real-world experiments on a physical UAV platform provide preliminary evidence of sim-to-real transferability, and our dataset and code are publicly available.