High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms. While aerial robotics simulators have evolved to support specialized needs such as fidelity, differentiability, and swarms independently, a unified platform that can synthesize data across all these domains is missing. In this work, we propose Crazyflow, a simulator designed to push the limits of aerial-robotics algorithm development, from model-based to data-driven methods, gradient-based to sampling-based approaches, and single-agent to multi-agent systems. Compared to existing state-of-the-art drone simulators, it achieves speeds more than an order of magnitude faster for a single drone and can simulate thousands of swarms of 4000 drones each. Real-world experiments show Crazyflow supports both analytical-gradient-based policy learning, achieving sub-centimeter trajectory tracking accuracy without domain randomization, and sampling-based obstacle avoidance at speeds exceeding half a billion steps per second. Breaking the traditional train-then-deploy paradigm, we show that its unprecedented speed even enables in-flight reinforcement learning; we demonstrate this by throwing a physical drone into the air and training a recovery policy from scratch in 0.38 seconds, successfully stabilizing the drone. Crazyflow supports multiple levels of simulation abstraction, is directly compatible with all open-source Crazyflie models, and enables rapid reconfiguration across custom drone platforms and applications by providing a light-weight system identification pipeline. By pushing accuracy, speed, and differentiability simultaneously, Crazyflow serves as an open-source resource for synthetic data generation, with emerging capabilities for large-scale parallelization for online, in-execution learning and optimization, opening the door to novel algorithm development.

翻译：高质量、大规模的仿真合成数据正成为推动机器人算法能力提升的基石。尽管空中机器人仿真器已各自独立发展为支持保真度、可微分性和群体等特殊需求，但尚缺乏一个能统一跨所有领域合成数据的平台。本文提出Crazyflow，该仿真器旨在突破空中机器人算法开发的边界，涵盖从基于模型到数据驱动、梯度方法到采样方法、单智能体到多智能体系统的各类方法。与现有最先进的无人机仿真器相比，Crazyflow单机速度提升超过一个数量级，并能仿真由4000架无人机组成的数千个机群。真实世界实验表明，Crazyflow既支持基于解析梯度的策略学习（无需域随机化即可实现亚厘米级轨迹跟踪精度），也支持基于采样的避障（速度超过每秒5亿步）。突破传统的"先训练再部署"范式，其前所未有的速度甚至实现了飞行中强化学习：我们将物理无人机抛向空中，在0.38秒内从零开始训练恢复策略，成功稳定了无人机。Crazyflow支持多层级仿真抽象，可直接兼容所有开源Crazyflie模型，并通过提供轻量级系统辨识流水线，实现跨自定义无人机平台和应用的快速重构。通过在精度、速度和可微分性上的同步突破，Crazyflow作为开源合成数据生成资源，具备大规模并行在线学习和优化的新兴能力，为新型算法开发开辟了道路。