Optical flow captures the motion of pixels in an image sequence over time, providing information about movement, depth, and environmental structure. Flying insects utilize this information to navigate and avoid obstacles, allowing them to execute highly agile maneuvers even in complex environments. Despite its potential, autonomous flying robots have yet to fully leverage this motion information to achieve comparable levels of agility and robustness. Challenges of control from optical flow include extracting accurate optical flow at high speeds, handling noisy estimation, and ensuring robust performance in complex environments. To address these challenges, we propose a novel end-to-end system for quadrotor obstacle avoidance using monocular optical flow. We develop an efficient differentiable simulator coupled with a simplified quadrotor model, allowing our policy to be trained directly through first-order gradient optimization. Additionally, we introduce a central flow attention mechanism and an action-guided active sensing strategy that enhances the policy's focus on task-relevant optical flow observations to enable more responsive decision-making during flight. Our system is validated both in simulation and the real world using an FPV racing drone. Despite being trained in a simple environment in simulation, our system is validated both in simulation and the real world using an FPV racing drone. Despite being trained in a simple environment in simulation, our system demonstrates agile and robust flight in various unknown, cluttered environments in the real world at speeds of up to 6m/s.

翻译：光流捕捉图像序列中像素随时间的变化，提供关于运动、深度和环境结构的信息。飞行昆虫利用此类信息进行导航与避障，使其能在复杂环境中执行高度敏捷的机动动作。尽管具备潜力，自主飞行机器人尚未充分利用此类运动信息以实现同等的敏捷性与鲁棒性。基于光流的控制面临诸多挑战，包括高速条件下提取准确光流、处理含噪声的估计值，以及在复杂环境中确保鲁棒性能。为应对这些挑战，我们提出一种基于单目光流的四旋翼飞行器避障端到端系统。我们开发了结合简化四旋翼模型的高效可微分仿真器，使策略能够通过一阶梯度优化直接训练。此外，我们引入中心流注意力机制与动作引导的主动感知策略，增强策略对任务相关光流观测的关注度，从而在飞行过程中实现更灵敏的决策。我们的系统通过FPV竞速无人机在仿真环境与真实世界中得到验证。尽管仅在仿真简单环境中训练，该系统在真实世界多种未知杂乱环境中仍展现出敏捷且鲁棒的飞行能力，最高速度可达6米/秒。