RAPID: 基于逆强化学习的鲁棒且敏捷规划器用于基于视觉的无人机导航
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation
AI总结：
本文提出基于逆强化学习的无人机高速视觉导航规划器，通过减少环境交互和提升鲁棒性，实现复杂环境中的敏捷飞行。
摘要：
本文介绍了一种基于学习的视觉规划器，用于在复杂环境中实现敏捷无人机飞行。所提出的规划器能够在毫秒内生成无碰撞的航点，使无人机能够在复杂环境中执行敏捷动作，而无需构建独立的感知、制图和规划模块。基于学习的方法，如行为克隆（BC）和强化学习（RL），在视觉导航中表现出有希望的性能，但仍面临固有的限制。BC由于专家模仿有限而容易累积误差，而RL在奖励函数设计和样本效率方面存在困难。为了解决这些限制，本文提出了一种基于逆强化学习（IRL）的框架，用于高速视觉导航。通过利用IRL，可以减少与模拟环境的交互次数，并在处理高维空间的同时保持RL策略的鲁棒性。基于运动原语的路径规划算法通过从不同环境中获取特权地图数据收集专家数据集，确保全面的场景覆盖。通过利用从智能体与模拟环境交互中获得的专家和学习者数据集，跨多样状态学习出鲁棒的奖励函数和策略。虽然所提出的方法仅在模拟环境中训练，但可以直接应用于现实世界场景，而无需额外的训练或调整。所提出方法的性能在模拟和现实环境中得到验证，包括森林和各种结构。训练的策略在现实飞行实验中实现了平均速度7米/秒和最大速度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工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
