# WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning

arXiv: https://arxiv.org/abs/2607.01281 · HTML: https://mlsanity.com/paper/2607.01281/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025)
- [Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation](https://mlsanity.com/paper/2307.05263/) (2023)
- [Reinforcement Learning based Autonomous Multi-Rotor Landing on Moving Platforms](https://mlsanity.com/paper/2302.13192/) (2023)
- [Robust Reinforcement Learning Algorithm for Vision-based Ship Landing of UAVs](https://mlsanity.com/paper/2209.08381/) (2022)
- [Landing a UAV in Harsh Winds and Turbulent Open Waters](https://mlsanity.com/paper/2301.00255/) (2022)
- [Quadrotor Autonomous Landing on Moving Platform](https://mlsanity.com/paper/2208.05201/) (2022)
- [A Manipulator-Assisted Multiple UAV Landing System for USV Subject to Disturbance](https://mlsanity.com/paper/2212.12196/) (2022)
- [Lander.AI: Adaptive Landing Behavior Agent for Expertise in 3D Dynamic Platform Landings](https://mlsanity.com/paper/2403.06572/) (2024)
- [Coastal Underwater Evidence Search System with Surface-Underwater Collaboration](https://mlsanity.com/paper/2410.02345/) (2024)

## Built on by (most cited first, 2 of 2)

- [Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck](https://mlsanity.com/paper/2610.03924/) (2026)
- [Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States](https://mlsanity.com/paper/2610.13190/) (2026)

## Abstract (arXiv)

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.