# WorldFly: A World-Model-Based Vision-Language-Action Model for UAV Navigation

arXiv: https://arxiv.org/abs/2606.06147 · HTML: https://mlsanity.com/paper/2606.06147/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 13 of its 14 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)

## Builds on (most cited first, 13 of 13)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [WorldVLA: Towards Autoregressive Action World Model](https://mlsanity.com/paper/2506.21539/) (2025)
- [Navigation World Models](https://mlsanity.com/paper/2412.03572/) (2024)
- [Unified Video Action Model](https://mlsanity.com/paper/2503.00200/) (2025)
- [Matrix-game 2.0: An open-source real-time and streaming interactive world model](https://mlsanity.com/paper/2508.13009/) (2025)
- [Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation](https://mlsanity.com/paper/2508.05635/) (2025)
- [Yume: An Interactive World Generation Model](https://mlsanity.com/paper/2507.17744/) (2025)
- [VideoVLA: Video Generators Can Be Generalizable Robot Manipulators](https://mlsanity.com/paper/2512.06963/) (2025)
- [Openfly: A comprehensive platform for aerial vision-language navigation](https://mlsanity.com/paper/2502.18041/) (2025)
- [UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning](https://mlsanity.com/paper/2505.15725/) (2025)
- [RaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour](https://mlsanity.com/paper/2503.02572/) (2025)

## Built on by (most cited first, 6 of 6)

- [WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN](https://mlsanity.com/paper/2608.07267/) (2026)
- [Map the Possibilities: Spatial Belief Fields for Language-Goal Aerial Navigation](https://mlsanity.com/paper/2609.05841/) (2026)
- [UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents](https://mlsanity.com/paper/2609.18326/) (2026)
- [DroneWAM: Efficient World Action Model for Drone Visual Navigation](https://mlsanity.com/paper/2609.33148/) (2026)
- [ForeFly: A Dual-Horizon World Action Model for Aerial Vision-Language Navigation](https://mlsanity.com/paper/2609.33581/) (2026)
- [DiffWAM: A Fast and Efficient Navigation World Action Model](https://mlsanity.com/paper/2609.39763/) (2026)

## Abstract (arXiv)

End-to-end Vision-Language-Action (VLA) models have shown promise in UAV navigation. However, existing approaches typically rely on historical observations to directly predict actions, often struggling in dense urban environments where severe occlusions and sharp turns result in drastic viewpoint transitions. We argue that the ability to "imagine" future states -- inherent in World Models -- is critical for robust decision-making under such partial observability. To address this, we construct a challenging Urban Canyon Traversal Benchmark, specifically designed to evaluate spatial understanding in scenarios characterized by severe occlusions and drastic viewpoint transitions. To this end, we propose WorldFly, a novel world-model-based VLA framework that employs a dual-branch coupled flow matching mechanism to jointly generate future video predictions and navigation actions, thereby explicitly guiding the agent's policy via spatial imagination. Extensive evaluations on our benchmark demonstrate that WorldFly outperforms other baselines, particularly in unseen environments, validating the effectiveness of integrating world models into embodied aerial agents.