Agile flight for the quadrotor cable-suspended payload system is a formidable challenge due to its underactuated, highly nonlinear, and hybrid dynamics. Traditional optimization-based methods often struggle with high computational costs and the complexities of cable mode transitions, limiting their real-time applicability and maneuverability exploitation. In this letter, we present FLARE, a reinforcement learning (RL) framework that directly learns agile navigation policy from high-fidelity simulation. Our method is validated across three designed challenging scenarios, notably outperforming a state-of-the-art optimization-based approach by a 3x speedup during gate traversal maneuvers. Furthermore, the learned policies achieve successful zero-shot sim-to-real transfer, demonstrating remarkable agility and safety in real-world experiments, running in real time on an onboard computer.

翻译：四旋翼缆索悬挂负载系统的敏捷飞行因其欠驱动、高度非线性及混合动力学特性而构成严峻挑战。传统基于优化的方法常受限于高计算成本与缆索模式切换的复杂性，制约了其实时适用性与机动性开发。本文提出FLARE，一种从高保真仿真中直接学习敏捷导航策略的强化学习框架。该方法在三种设计的挑战性场景中得到验证，在穿越门框机动中显著优于当前最先进的优化方法，速度提升达3倍。此外，习得的策略成功实现了零样本仿真到现实的迁移，在搭载板载计算机的实时运行中，于真实世界实验中展现出卓越的敏捷性与安全性。