# ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering

arXiv: https://arxiv.org/abs/2606.14772 · HTML: https://mlsanity.com/paper/2606.14772/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://mlsanity.com/paper/2412.03555/) (2024)
- [ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model](https://mlsanity.com/paper/2502.14420/) (2025)
- [Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology](https://mlsanity.com/paper/2410.07087/) (2024)
- [ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge](https://mlsanity.com/paper/2505.21906/) (2025)
- [UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents](https://mlsanity.com/paper/2508.00288/) (2025)
- [CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space](https://mlsanity.com/paper/2502.12532/) (2025)
- [UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning](https://mlsanity.com/paper/2505.15725/) (2025)
- [Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering](https://mlsanity.com/paper/2503.11117/) (2025)
- [VLA-AN: An Efficient and Onboard Vision-Language-Action Framework for Aerial Navigation in Complex Environments](https://mlsanity.com/paper/2512.15258/) (2025)
- [FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models](https://mlsanity.com/paper/2505.12835/) (2025)
- [AirCopBench: A Benchmark for Multi-drone Collaborative Embodied Perception and Reasoning](https://mlsanity.com/paper/2511.11025/) (2025)
- [Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?](https://mlsanity.com/paper/2602.07055/) (2026)
- [EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering](https://mlsanity.com/paper/2410.20263/) (2024)
- [SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environments](https://mlsanity.com/paper/2507.06564/) (2025)
- [IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios](https://mlsanity.com/paper/2505.20640/) (2025)
- [Memory-Centric Embodied Question Answering](https://mlsanity.com/paper/2505.13948/) (2025)
- [CoV: Chain-of-View Prompting for Spatial Reasoning](https://mlsanity.com/paper/2601.05172/) (2026)
- [Active Zero: Self-Evolving Vision-Language Models through Active Environment Exploration](https://mlsanity.com/paper/2602.11241/) (2026)
- [LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation](https://mlsanity.com/paper/2602.12351/) (2026)
- [BridgeEQA: Virtual Embodied Agents for Real Bridge Inspections](https://mlsanity.com/paper/2511.12676/) (2025)

## Built on by (most cited first, 1 of 1)

- [UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents](https://mlsanity.com/paper/2609.18326/) (2026)

## Abstract (arXiv)

Aerial Embodied Question Answering (EQA) requires Unmanned Aerial Vehicles (UAVs) to actively perceive the environment and answer natural language questions. Existing outdoor EQA systems usually stop once the target enters the UAV's field of view, leaving the fine-grained viewpoint adjustment needed for evidence-seeking questions largely unresolved. To address this issue, we introduce FG-EQA, a fine-grained active perception EQA benchmark with more than 40K simulated trajectories and 1K real-world trajectories. Drawing inspiration from the ``waggle dance'' of scout bees, which iteratively adjust their flight paths to verify target information, we propose ScoutVLA, an evidence-driven Vision-Language-Action model for outdoor EQA. To emulate this active exploration behavior, ScoutVLA features a decoupled dual-expert architecture: a vision-language expert infers the semantic intent to identify missing evidence, while an independent action expert employs high-DoF flow matching to generate continuous viewpoint-refinement trajectories. To balance the competing demands of continuous control and semantic reasoning, we devise a decoupled training strategy with a knowledge insulation mechanism that prevents the action gradients from erasing the model's multimodal reasoning ability. Extensive simulated experiments and a qualitative real-world field study both verify the superiority of ScoutVLA over the state-of-the-art baselines, demonstrating a 10.48boldsymbol× higher average strict success rate and a 7.72boldsymbol× higher average QA correctness.