RaceVLA：基于VLA的具有类人行为的竞速无人机导航
RaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour
AI总结：
RaceVLA利用视觉-语言-动作模型模拟人类飞行员决策，实现自主竞速无人机导航，在复杂高速场景中展现出优于OpenVLA和RT-2的实时调整能力与鲁棒泛化性。
摘要：
RaceVLA提出了一种自主竞速无人机导航的创新方法，通过利用视觉-语言-动作（VLA）来模拟类人行为。本研究探索了先进算法的集成，使无人机能够基于实时环境反馈调整其导航策略，模仿人类飞行员的决策过程。该模型在收集的竞速无人机数据集上进行了微调，尽管竞速无人机环境复杂，但仍展现出强大的泛化能力。得益于动态相机和简化的运动任务，RaceVLA在运动泛化（75.0对60.0）和语义泛化（45.5对36.3）上优于OpenVLA。然而，由于在具有不同物体尺寸的动态环境中机动的挑战，视觉（79.6对87.0）和物理（50.0对76.7）泛化略有下降。RaceVLA还在所有轴上优于RT-2——视觉（79.6对52.0）、运动（75.0对55.0）、物理（50.0对26.7）和语义（45.5对38.8），证明了其在复杂环境中进行实时调整的鲁棒性。实验显示平均速度为1.04 m/s，最大速度为2.02 m/s，且机动性一致，证明了RaceVLA有效处理高速场景的能力。这些发现突显了RaceVLA在竞技竞速环境中进行高性能导航的潜力。RaceVLA的代码库、预训练权重和数据集可在http URL获取：https://racevla.github.io/
英文摘要：
RaceVLA presents an innovative approach for autonomous racing drone navigation by leveraging Visual-Language-Action (VLA) to emulate human-like behavior. This research explores the integration of advanced algorithms that enable drones to adapt their navigation strategies based on real-time environmental feedback, mimicking the decision-making processes of human pilots. The model, fine-tuned on a collected racing drone dataset, demonstrates strong generalization despite the complexity of drone racing environments. RaceVLA outperforms OpenVLA in motion (75.0 vs 60.0) and semantic generalization (45.5 vs 36.3), benefiting from the dynamic camera and simplified motion tasks. However, visual (79.6 vs 87.0) and physical (50.0 vs 76.7) generalization were slightly reduced due to the challenges of maneuvering in dynamic environments with varying object sizes. RaceVLA also outperforms RT-2 across all axes - visual (79.6 vs 52.0), motion (75.0 vs 55.0), physical (50.0 vs 26.7), and semantic (45.5 vs 38.8), demonstrating its robustness for real-time adjustments in complex environments. Experiments revealed an average velocity of 1.04 m/s, with a maximum speed of 2.02 m/s, and consistent maneuverability, demonstrating RaceVLA's ability to handle high-speed scenarios effectively. These findings highlight the potential of RaceVLA for high-performance navigation in competitive racing contexts. The RaceVLA codebase, pretrained weights, and dataset are available at this http URL: https://racevla.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
