WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning
This paper proposes WaveLander, a hierarchical reinforcement learning framework for autonomous UAV landing on wave-disturbed marine platforms. It decouples vertical landing decision-making from low-level flight stabilization, using an RL policy to output a vertical velocity reference while a conventional controller handles attitude and lateral tracking. Simulations show robust performance and generalization to unseen disturbances.
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[Submitted on 1 Jul 2026]
Title:WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning
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Abstract:Autonomous landing of unmanned aerial vehicles (UAVs) on wave-disturbed marine platforms remains challenging due to stochastic platform motion, time-varying platform attitude, and uncertain touchdown conditions. Existing model-based methods often require accurate motion prediction and online optimization, while end-to-end learning approaches may suffer from high training complexity and limited interpretability. This paper presents WaveLander, a hierarchical control framework via reinforcement learning (RL) that decouples vertical landing decision-making from low-level flight stabilization. The RL policy maps a compact platform-relative observation to a scalar vertical velocity reference, while a conventional low-level flight controller maintains attitude stability and lateral tracking. This formulation reduces dynamic platform landing to a low-dimensional, timing-aware control problem and enables smooth landing behavior without explicit switching rules. Simulation results under randomized wave-induced platform motions show that WaveLander achieves robust landing performance and generalizes to unseen disturbance conditions, demonstrating the potential of hierarchical learning-based control for marine UAV recovery.
Comments: 8 pages, 6 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.01281 [cs.RO]
(or arXiv:2607.01281v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.01281
arXiv-issued DOI via DataCite
Submission history
From: Chun-Kit Li [view email] [v1] Wed, 1 Jul 2026 08:28:15 UTC (10,190 KB)
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