Flight Mastery in Turbulent Skies: Shared Control and Curriculum Reinforcement Learning for Crosswind Landing

Landing in crosswind conditions poses significant challenges for aircraft, as traditional control methods often fail to ensure stability in rapidly changing wind environments. While reinforcement learning offers a promising alternative, it typically suffers from low sample efficiency and limited generalization under stochastic wind fields. To address these challenges, we propose a Shared Control and Curriculum Reinforcement Learning framework. We model the crosswind landing task as a Markov Decision Process (MDP), explicitly defining the state space, action space, and wind field representation. To initialize learning, we decompose the multi-objective landing task into four sub-tasks—altitude, attitude, heading, and speed control—and train expert policies for each. These are then distilled into a shared control model via behavior cloning, providing a pre-trained policy with basic flight control capabilities. We further fine-tune this model using curriculum reinforcement learning, progressively increasing the complexity of wind conditions to enhance robustness and generalization. Experimental results across multiple aircraft and wind scenarios show that our method improves landing success rates and trajectory smoothness, while generalizing more effectively to unseen wind conditions, outperforming PID controllers, imitation learning, and mainstream RL baselines.