Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit their capabilities. Autonomous operation with onboard sensing and computation has been limited to low speeds. State-of-the-art methods generally separate the navigation problem into subtasks: sensing, mapping, and planning. Although this approach has proven successful at low speeds, the separation it builds upon can be problematic for high-speed navigation in cluttered environments. The subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline. Here, we propose an end-to-end approach that can autonomously fly quadrotors through complex natural and human-made environments at high speeds with purely onboard sensing and computation. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. This direct mapping drastically reduces processing latency and increases robustness to noisy and incomplete perception. The sensorimotor mapping is performed by a convolutional network that is trained exclusively in simulation via privileged learning: imitating an expert with access to privileged information. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments that were never experienced during training: dense forests, snow-covered terrain, derailed trains, and collapsed buildings. Our work demonstrates that end-to-end policies trained in simulation enable high-speed autonomous flight through challenging environments, outperforming traditional obstacle avoidance pipelines.

中文翻译：

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在野外学习高速飞行

四旋翼是敏捷的。与大多数其他机器不同，它们可以高速穿越极其复杂的环境。迄今为止，只有专业的人类飞行员才能充分发挥他们的能力。带有机载传感和计算的自主操作仅限于低速。最先进的方法通常将导航问题分为子任务：传感、映射和规划。尽管这种方法已被证明在低速下是成功的，但它所建立的分离对于在杂乱环境中的高速导航可能是有问题的。子任务按顺序执行，从而导致处理延迟增加和流水线中的错误复合。这里，我们提出了一种端到端的方法，可以在复杂的自然和人造环境中以纯机载传感和计算自主飞行四旋翼飞行器。关键原理是以后退的方式将嘈杂的感官观察直接映射到无碰撞轨迹。这种直接映射大大减少了处理延迟并增加了对嘈杂和不完整感知的鲁棒性。感觉运动映射由卷积网络执行，该网络通过特权学习专门在模拟中训练：模仿可以访问特权信息的专家。通过模拟真实的传感器噪声，我们的方法实现了从模拟到训练期间从未经历过的具有挑战性的现实世界环境的零射击转移：茂密的森林、积雪覆盖的地形、脱轨的火车、和倒塌的建筑物。我们的工作表明，在模拟中训练的端到端策略能够在具有挑战性的环境中实现高速自主飞行，优于传统的避障管道。