This paper introduces a learning-based visual planner for agile drone flight in cluttered environments. The proposed planner generates collision-free waypoints in milliseconds, enabling drones to perform agile maneuvers in complex environments without building separate perception, mapping, and planning modules. Learning-based methods, such as behavior cloning (BC) and reinforcement learning (RL), demonstrate promising performance in visual navigation but still face inherent limitations. BC is susceptible to compounding errors due to limited expert imitation, while RL struggles with reward function design and sample inefficiency. To address these limitations, this paper proposes an inverse reinforcement learning (IRL)-based framework for high-speed visual navigation. By leveraging IRL, it is possible to reduce the number of interactions with simulation environments and improve capability to deal with high-dimensional spaces while preserving the robustness of RL policies. A motion primitive-based path planning algorithm collects an expert dataset with privileged map data from diverse environments, ensuring comprehensive scenario coverage. By leveraging both the acquired expert and learner dataset gathered from the agent's interactions with the simulation environments, a robust reward function and policy are learned across diverse states. While the proposed method is trained in a simulation environment only, it can be directly applied to real-world scenarios without additional training or tuning. The performance of the proposed method is validated in both simulation and real-world environments, including forests and various structures. The trained policy achieves an average speed of 7 m/s and a maximum speed of 8.8 m/s in real flight experiments. To the best of our knowledge, this is the first work to successfully apply an IRL framework for high-speed visual navigation of drones.

翻译：本文提出了一种基于学习的视觉规划器，用于在杂乱环境中实现敏捷无人机飞行。所提出的规划器能够在毫秒级时间内生成无碰撞航点，使无人机能够在复杂环境中执行敏捷机动，而无需构建独立的感知、建图和规划模块。基于学习的方法，如行为克隆（BC）和强化学习（RL），在视觉导航中展现出有前景的性能，但仍面临固有局限。BC因专家模仿有限而易受误差累积影响，而RL则受限于奖励函数设计和样本效率低下。为应对这些局限，本文提出了一种基于逆强化学习（IRL）的高速视觉导航框架。通过利用IRL，可以在保持RL策略鲁棒性的同时，减少与仿真环境的交互次数，并提升处理高维空间的能力。一种基于运动基元的路径规划算法从多样环境中收集带有特权地图数据的专家数据集，确保全面覆盖不同场景。通过结合从智能体与仿真环境交互中获取的专家和学习者数据集，学习得到适用于多样状态的鲁棒奖励函数和策略。尽管所提方法仅在仿真环境中训练，但无需额外训练或调参即可直接应用于真实场景。该方法在仿真和真实环境（包括森林和多种结构）中均验证了性能。在真实飞行实验中，训练后的策略实现了平均速度7米/秒，最高速度8.8米/秒。据我们所知，这是首次成功将IRL框架应用于无人机高速视觉导航的研究。