Perching at speed is among the most demanding flight behaviours that birds perform^1,2 and is beyond the capability of most autonomous vehicles. Smaller birds may touch down by hovering^3,4,5,6,7,8, but larger birds typically swoop up to perch^1,2—presumably because the adverse scaling of their power margin prohibits hovering⁹ and because swooping upwards transfers kinetic to potential energy before collision^1,2,10. Perching demands precise control of velocity and pose^11,12,13,14, particularly in larger birds for which scale effects make collisions especially hazardous^6,15. However, whereas cruising behaviours such as migration and commuting typically minimize the cost of transport or time of flight¹⁶, the optimization of such unsteady flight manoeuvres remains largely unexplored^7,17. Here we show that the swooping trajectories of perching Harris’ hawks (Parabuteo unicinctus) minimize neither time nor energy alone, but rather minimize the distance flown after stalling. By combining motion capture data from 1,576 flights with flight dynamics modelling, we find that the birds’ choice of where to transition from powered dive to unpowered climb minimizes the distance over which high lift coefficients are required. Time and energy are therefore invested to provide the control authority needed to glide safely to the perch, rather than being minimized directly as in technical implementations of autonomous perching under nonlinear feedback control¹² and deep reinforcement learning^18,19. Naive birds learn this behaviour on the fly, so our findings suggest a heuristic principle that could guide reinforcement learning of autonomous perching.

高速栖息是鸟类执行的最苛刻的飞行行为之一^1,2并且超出了大多数自动驾驶汽车的能力。较小的鸟类可能会通过悬停^3,4,5,6,7,8着陆，但较大的鸟类通常会俯冲到栖息地^1,2——大概是因为它们的功率裕度的不利缩放阻止了悬停⁹并且因为向上俯冲将动能转移到碰撞前^{的势能 1,2,10} . 栖息需要精确控制速度和姿势^11,12,13,14，尤其是在规模效应使碰撞特别危险的大型鸟类中^6,15. 然而，虽然诸如迁移和通勤等巡航行为通常可以最大限度地减少运输成本或飞行时间¹⁶，但这种不稳定飞行机动的优化仍然在很大程度上未探索^7,17。在这里，我们展示了栖息的哈里斯鹰 ( Parabuteo unicinctus ) 的俯冲轨迹) 既不能单独减少时间也不能减少能量，而是尽量减少失速后的飞行距离。通过将 1,576 次飞行的动作捕捉数据与飞行动力学建模相结合，我们发现鸟类选择从动力潜水到无动力爬升的过渡位置可以最大限度地减少需要高升力系数的距离。因此，投入时间和精力来提供安全滑翔到栖息处所需的控制权限，而不是像在非线性反馈控制¹²和深度强化学习^18,19下的自主栖息的技术实施中那样直接最小化。天真的鸟类会在飞行中学习这种行为，因此我们的研究结果提出了一种启发式原则，可以指导自主栖息的强化学习。