ActiveFly-Bench：将具身问答与视觉-语言-动作对齐用于空中具身感知
ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception
- Tsinghua University（清华大学）
- Manifold AI（流形人工智能）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
介绍用于无人机具身感知的ActiveFly-Bench基准测试，它分解主动感知为三个层次任务，收集数据集，开发集成视觉语言推理与细粒度控制的ActiveFly智能体，实验表明当前无人机智能体有困难，该基准成为具身空中智能新测试平台。
AI中文摘要：
我们引入了ActiveFly-Bench，这是首个用于无人机具身感知的、连接网络空间推理与物理世界交互的基准测试。该基准将主动感知分解为三个层次任务：空中具身问答（Air-EQA）、观察行为规划（OBP）和细粒度语言引导的无人机控制（FLUC），明确连接高级任务理解、行为规划和低级控制。数据集从真实和模拟户外环境收集用于训练和评估。我们还开发了ActiveFly，一个将视觉语言推理与细粒度控制集成的闭环无人机智能体，并将其部署在物理无人机平台上。实验表明当前无人机智能体在主动感知的行为规划、视角调整和稳健任务完成方面仍有困难。这些结果使ActiveFly-Bench成为具身空中智能的新测试平台。
英文摘要：
We introduce ActiveFly-Bench, the first benchmark to bridge cyberspace reasoning and physical-world interaction for UAV embodied perception. The benchmark decomposes active perception into three hierarchical tasks: Aerial Embodied Question Answering (Air-EQA), Observation Behavior Planning (OBP), and Fine-grained Language-guided UAV Control (FLUC), explicitly connecting high-level task understanding, behavior planning, and low-level control. The datasets are collected from both real-world and simulated outdoor environments for training and evaluation. We further develop ActiveFly, a closed-loop UAV agent that integrates visual-language reasoning with fine-grained control, and deploy it on a physical UAV platform. Experiments with representative VLMs and VLA models show that current UAV agents still struggle with behavior planning, viewpoint adjustment, and robust task completion in active perception. These results establish ActiveFly-Bench as a new testbed for embodied aerial intelligence.
