Agile maneuvering of the quadrotor cable-suspended system is significantly hindered by its non-smooth hybrid dynamics. While model-free Reinforcement Learning (RL) circumvents explicit differentiation of complex models, achieving attitude-constrained or inverted flight remains an open challenge due to the extreme reward sparsity under strict orientation requirements. This paper presents ASTER, a robust RL framework that achieves, to our knowledge, the first successful autonomous inverted flight for the cable-suspended system. We propose hybrid-dynamics-informed state seeding (HDSS), an initialization strategy that back-propagates target configurations through physics-consistent kinematic inversions across both taut and slack cable phases. HDSS enables the policy to discover aggressive maneuvers that are unreachable via standard exploration. Extensive simulations and real-world experiments demonstrate remarkable agility, precise attitude alignment, and robust zero-shot sim-to-real transfer across complex trajectories.

翻译：四旋翼缆绳悬吊系统的敏捷机动因其非光滑混合动力学特性而受到严重制约。尽管无模型强化学习避免了复杂模型的显式微分，但在严格姿态约束下，由于奖励极度稀疏，实现姿态约束飞行或倒置飞行仍是一个开放挑战。本文提出ASTER，一个鲁棒的强化学习框架，据我们所知，首次成功实现了悬吊系统的自主倒置飞行。我们提出混合动力学启发的状态初始化策略，该策略通过物理一致的逆运动学，在缆绳张紧与松弛两相中反向传播目标构型。该策略使策略能够发现标准探索无法实现的激进机动动作。大量仿真与实物实验证明了该系统在复杂轨迹上具有卓越的敏捷性、精确的姿态对准能力以及鲁棒的零样本仿真到现实迁移性能。