梦想飞翔：基于模型的强化学习用于基于视觉的无人机飞行
Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight
AI总结：
本文提出基于模型的强化学习方法，通过像素观测实现无人机敏捷飞行，展示了在模拟和真实世界中的高效表现，推动了基于像素的自主飞行技术发展。
摘要：
自动驾驶无人机竞速已成为测试学习、感知、规划和控制极限的挑战性机器人基准。专家人类飞行员能通过将单个摄像头的像素直接映射到控制命令来飞越赛道。最近的自动驾驶无人机竞速工作尝试直接像素到命令的控制策略，依赖于中间表示简化观测空间或使用模仿学习进行大量强化学习。本文利用DreamerV3训练能够通过仅使用像素作为观测的视觉-运动策略，实现赛道上的敏捷飞行。与PPO或SAC等无模型方法相比，我们的方法在样本效率低且在该设置中挣扎的情况下，从像素中获得无人机竞速技能。值得注意的是，一种感知意识的行为，即主动将摄像头转向纹理丰富的门区域，无需手工设计的奖励项。我们的实验显示，在模拟和真实世界飞行中使用硬件在环设置和渲染图像观测，所提出的方法如何在真实四旋翼无人机上以高达9 m/s的速度部署。这些结果推进了基于像素的自主飞行状态，并证明了MBRL为现实世界机器人研究提供了一条有希望的路径。
英文摘要：
Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert human pilots are able to fly a drone through a race track by mapping pixels from a single camera directly to control commands. Recent works in autonomous drone racing attempting direct pixel-to-commands control policies have relied on either intermediate representations that simplify the observation space or performed extensive bootstrapping using Imitation Learning (IL). This paper leverages DreamerV3 to train visuomotor policies capable of agile flight through a racetrack using only pixels as observations. In contrast to model-free methods like PPO or SAC, which are sample-inefficient and struggle in this setting, our approach acquires drone racing skills from pixels. Notably, a perception-aware behaviour of actively steering the camera toward texture-rich gate regions emerges without the need of handcrafted reward terms for the viewing direction. Our experiments show in both, simulation and real-world flight using a hardware-in-the-loop setup with rendered image observations, how the proposed approach can be deployed on real quadrotors at speeds of up to 9 m/s. These results advance the state of pixel-based autonomous flight and demonstrate that MBRL offers a promising path for real-world robotics research.
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。
