Animals learn to adapt agility of their movements to their capabilities and
the environment they operate in. Mobile robots should also demonstrate this
ability to combine agility and safety. The aim of this work is to endow flight
vehicles with the ability of agility adaptation in prior unknown and partially
observable cluttered environments. We propose a hierarchical learning and
planning framework where we utilize both trial and error to comprehensively
learn an agility policy with the vehicle's observation as the input, and
well-established methods of model-based trajectory generation. Technically, we
use online model-free reinforcement learning and a pre-training-fine-tuning
reward scheme to obtain the deployable policy. The statistical results in
simulation demonstrate the advantages of our method over the constant agility
baselines and an alternative method in terms of flight efficiency and safety.
In particular, the policy leads to intelligent behaviors, such as perception
awareness, which distinguish it from other approaches. By deploying the policy
to hardware, we verify that these advantages can be brought to the real world.

通过层次学习和规划框架，利用试错和模型生成轨迹的方法，本研究通过在线无模型强化学习和预训练微调奖励机制来赋予飞行器在未知和部分可观察环境中适应敏捷性的能力，并在仿真和实际硬件验证中证明了该方法比常数敏捷度基准和替代方法在飞行效率和安全性方面的优势。