外观
论文arXiv 2610.0852622 天前
WareFly-VLA：面向智能仓库中无人机导航与人体跟踪的视觉-语言-动作框架
原文链接：WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses（arXiv 2610.08526 · 发布日期：2026-10-06）
WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses
提出WareFly-VLA框架与数据集，用语言引导无人机搜索、定位和跟踪仓库人员，并在Isaac Sim仿真数据上评测开源VLA。
核心要点
- 智能仓库中无人机语言条件控制缺少同时含低层动作、细粒度描述和真实工业环境的基准。
- 数据集含507段人工遥操作飞行和8504个RGB转移，配目标外观描述与四自由度控制命令。
- 在四种开源VLA架构和两种控制频率下评测，严格泛化设置中性能显著下降。
- 同步的视频、语言、动作、位姿和难度标注支持世界模型研究，数据集、基线和评测协议已发布。
验证范围与局限：验证范围为NVIDIA Isaac Sim中的仓库仿真，覆盖目标接近和人员跟随两类无人机任务。
英文摘要 (Abstract)
Vision-Language-Action (VLA) models have achieved impressive results in robotic manipulation and ground-mobile navigation, yet language-conditioned control of unmanned aerial vehicles (UAVs) in smart warehouses remains largely unexplored, hindered by the lack of benchmarks that jointly provide continuous low-level flight actions, fine-grained natural-language target descriptions, and realistic industrial environments. This paper introduces WareFly-VLA, a photorealistic UAV VLA framework and dataset for language-guided human search, localization, and tracking in warehouse environments. It contains 507 human-teleoperated flight episodes and 8,504 high-resolution RGB transitions collected in NVIDIA Isaac Sim, each paired with a human-written appearance description of the target worker and a synchronized four-degree-of-freedom control command. Two aerial tasks are covered: target approach and person following, under occlusion, long-range search, altitude variation, and clutter. A unified benchmark of four open-source VLA architectures (SmolVLA, GR00T N1.7, pi_0 and OpenVLA) is established under a leakage-free episode-level protocol at two control rates. The results show that language-conditioned aerial control in warehouses is far from solved: performance drops substantially under strict generalization settings, continuous action modeling consistently outperforms discrete action tokenization, only the forward channel is reliably learnable from a single frame, and current foundation-model interfaces transfer poorly from ground and humanoid embodiments to aerial platforms. The synchronized video, language, action, pose, and difficulty annotations further support world-model research. The dataset, baselines, and evaluation protocol are released to support language-grounded aerial autonomy in smart warehouses.
作者：Thinh D. Le, Son T. Nguyen, Duong Q. Nguyen, Dung D. Le, Ngo Anh Vien, H. Nguyen-Xuan
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