This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models. The proposed approach uses human demonstrations to train an initial model via imitation learning and then iteratively, improve its performance by using real-time human interventions. The aim of the interventions is to correct undesired behaviors and adapt the model to changes in the task dynamics. The learned model uncertainty is estimated in real-time via Monte Carlo Dropout and the human supervisor is cued for intervention via an audiovisual signal when this uncertainty exceeds a predefined threshold. This proposed approach is validated in an autonomous quadrotor landing task on both fixed and moving platforms. It is shown that with this algorithm, a human can rapidly teach a flight task to an unmanned aerial vehicle via demonstrating expert trajectories and then adapt the learned model by intervening when the learned controller performs any undesired maneuver, the task changes, and/or the model uncertainty exceeds a threshold

翻译：本文提出了一种从人类示教与干预中学习飞行控制系统的方法，同时考虑所学模型中的估计不确定性。所提方法利用人类示教，通过模仿学习训练初始模型，并通过实时人类干预迭代提升其性能。干预旨在纠正不良行为并使模型适应任务动态的变化。通过蒙特卡洛丢弃法实时估计所学模型的不确定性，当该不确定性超过预设阈值时，系统通过视听信号提示人类监督者进行干预。该方法的有效性在固定与移动平台上的自主四旋翼飞行器着陆任务中得到验证。结果表明，采用本算法，人类可通过演示专家轨迹快速教会无人飞行器飞行任务，并在学习控制器执行任何不良机动、任务发生变化或模型不确定性超过阈值时，通过干预对学习模型进行适配。