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

ACL ARR 2026 January Submission1568 Authors

Keywords: Unmanned Aerial Vehicle, Vision-and-Language Navigation, AirNav Dataset, Reinforcement Fine-Tuning, Real-World Testing

Abstract:

Existing Unmanned Aerial Vehicle (UAV) Vision-Language Navigation (VLN) datasets face issues such as dependence on virtual environments, lack of naturalness in instructions, and limited scale.
To address these challenges, we propose AirNav, a large-scale UAV VLN benchmark constructed from real urban aerial data, rather than synthetic environments, with natural and diverse instructions.
Additionally, we introduce the AirVLN-R1, which combines Supervised Fine-Tuning and Reinforcement Fine-Tuning to enhance performance and generalization.
The feasibility of the model is preliminarily evaluated through real-world tests.
Our dataset and code are publicly available.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Resources and Evaluation, Multimodality and Language Grounding to Vision, Robotics and Beyond

Contribution Types: NLP engineering experiment, Reproduction study, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English

Submission Number: 1568