This paper presents a reinforcement learning-based quadrotor navigation method that leverages efficient differentiable simulation, novel loss functions, and privileged information to navigate around large obstacles. Prior learning-based methods perform well in scenes that exhibit narrow obstacles, but struggle when the goal location is blocked by large walls or terrain. In contrast, the proposed method utilizes time-of-arrival (ToA) maps as privileged information and a yaw alignment loss to guide the robot around large obstacles. The policy is evaluated in photo-realistic simulation environments containing large obstacles, sharp corners, and dead-ends. Our approach achieves an 86% success rate and outperforms baseline strategies by 34%. We deploy the policy onboard a custom quadrotor in outdoor cluttered environments both during the day and night. The policy is validated across 20 flights, covering 589 meters without collisions at speeds up to 4 m/s.

翻译：本文提出了一种基于强化学习的四旋翼导航方法，该方法利用高效可微仿真、新型损失函数和特权信息来实现对大尺寸障碍物的规避。现有的基于学习的方法在存在狭窄障碍物的场景中表现良好，但当目标位置被大型墙体或地形阻挡时则面临困难。相比之下，所提方法利用到达时间（ToA）地图作为特权信息，并引入偏航角对齐损失来引导机器人绕行大型障碍物。策略在包含大型障碍物、急转弯和死角的逼真仿真环境中进行评估。我们的方法实现了86%的成功率，较基线策略性能提升34%。我们将策略部署于定制四旋翼飞行器的机载系统，在昼夜条件下的户外杂乱环境中进行测试。该策略经过20次飞行验证，累计飞行589米，最高速度达4米/秒，全程未发生碰撞。