Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck

Ritwik Shankar; Chiranjeev Prachand; Abhishek; Soumya Ranjan Sahoo

eess.SYOct 2, 2026

Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck

Authors: Ritwik Shankar, Chiranjeev Prachand, Abhishek, Soumya Ranjan Sahoo

Organizations: Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India · Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India

Abstract

This paper addresses autonomous landing of unmanned aerial vehicle (UAV) rotorcrafts on a heaving ship deck using a recurrent policy trained via asymmetric actor-critic reinforcement learning: the critic sees 6 s of future deck height during training, while the actor sees only what the onboard sensors provide in flight, a 17-dimensional state made of the vehicle's position, velocity and attitude relative to the deck and outputs world-frame velocity and yaw-rate commands. Training is performed across 4096 parallel simulated environments, followed by fine-tuning in 16 environments with an onboard vision pipeline in the loop. A control-barrier-function (CBF) stopping margin on the deck-relative vertical state is incorporated at two stages: as a reward term during training, where it halves the median simulated contact speed relative to a policy trained without it, and as a runtime safety filter at deployment, evaluated at every control step to abort and retry the descent when the margin is violated. Because the autopilot's disarm logic cannot detect the vehicle resting on a moving deck, proximity-based thrust cutoff at touchdown is commanded directly in the landing pipeline. The proposed approach is validated using a parallel-manipulator-platform-based deck emulator that reproduces the heaving motion of the ship deck (scaled to 0.70 m peak-to-peak, 7.5 s mean period) and a quadcopter UAV with an onboard camera. Across 31 motion-capture and 20 vision-based trials, the UAV landed every time, with median times to contact of 6.5 and 7.3 s; 77% and 50% landed on the first attempt, with mean deck-relative speeds of 0.29 and 0.27 m/s, respectively, at the instant of thrust cutoff, which is the last speed under the policy's control. Supplementary video: https://youtu.be/S5hDkrSZJt4

Figures & tables

Fig. 1 : Training and deployment pipeline of the proposed landing algorithm.

Panel of Figure 2 — Fig. 2 : (a) Quadrotor used for the heave-landing experiments. (b) Deck-relative vertical quantities used by the CBF filter.

Fig. 3 : Simulation ablation study of various methodologies.

Panel of Figure 4 — Fig. 4 : Vision-based RL landings on the heaving deck.

Fig. 5 : Landing sequence, hover to touchdown. Top: external view. Bottom: onboard camera with marker detections.

Fig. 6 : Deck-relative vertical velocity $|v_{\mathrm{rel},z}|$ at disarm $t_{\mathrm{d}}$ and at first contact $t_{\mathrm{TD}}$ .

Fig. 7 : Deck-relative closing speed from $0.5\,\mathrm{s}$ before to $0.5\,\mathrm{s}$ after disarm, median and 10–90 % band over 31 motion-capture and 20 vision landings. The second dotted line marks the median first contact, $0.20\,\mathrm{s}$ after disarm; landed runs settle to zero.

Fig. 8 : Deck-relative vertical speed at first contact, binned by deck speed at contact.

Sensing	Attempt 1	2	3	4	5
Mocap, $n{=}31$	24 (77%)	0	3 (10%)	2 (6%)	2 (6%)
Vision, $n{=}20$	10 (50%)	10 (50%)	0	0	0

TABLE I : Number of descents per landing.

UAV Landing on Moving Platforms

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May 22, 2026

Vision-Based Agile Landing on Turbulent Waters

Autonomous landing of Unmanned Aerial Vehicles on maritime vessels is challenging due to the coupled motion of the vehicle and landing platform in open-sea conditions. This paper presents a reinforcement-learning-based approach for autonomous multirotor landing on moving maritime platforms without requiring explicit platform-state observations or estimation during deployment. The proposed method uses multirotor state measurements together with local visual features, consisting of keypoints and associated descriptors extracted from the landing surface, to predict attitude and thrust commands. These commands are tracked by a conventional low-level controller. The policy is trained in simulation using synthetic keypoints with randomly generated normalized descriptors, enabling zero-shot deployment with different local feature extractors onboard the UAV. We evaluate the method in a realistic simulator and show that it outperforms a state-of-the-art Model Predictive Control baseline under platform motions corresponding to ''Very Rough'' sea conditions. Finally, we perform extensive real-world experiments, demonstrating autonomous onboard landing using two different local feature extractors. To the best of our knowledge, this is the first approach for agile multirotor landing on maritime platforms in turbulent waters that does not rely on an explicit platform-state during deployment.

Dimosthenis Angelis, Leonard Bauersfeld, Davide Scaramuzza +1

Department of Electrical and Photonics Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark · Robotics and Perception Group, Department of Informatics, University of Zurich, Zurich, Switzerland

RL for Robotics Aerial Robotics UAV Landing on Moving Platforms Quadrotor Control Vision-Based Robot Control

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Sep 11, 2026

Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings

Recovering unmanned aerial vehicles (UAVs) in maritime environments is challenging due to wind turbulence and ship-deck motion, making it a valuable test case for alternative control and learning approaches as conventional landing approaches often become unreliable. We study simulated mid-air capture of quadrotor UAVs by a ship-mounted robotic arm, learning robust cooperative control policies with Heterogeneous-Agent Proximal Policy Optimization (HAPPO) Reinforcement Learning. We train with HAPPO using a curriculum and an adversarial wind agent (HARL-AC) in NVIDIA Isaac Lab, and compare the obtained control policies against those generated through curriculum-based domain randomization and a benchmark trained on a single sea state. In-distribution evaluation on sea states

0/4/5

shows comparable success for HARL-AC and domain randomization of up to

97.5\%

. On out-of-distribution sea states

7/8/10

, HARL-AC generalizes better, achieving up to

16\%

higher median success rate at sea state 10, and substantially lower crash rates of up to

14\%

compared to the domain randomization policy. Furthermore, we show that the adversarially trained policy shows more cautious behavior, slightly increasing timeouts by

<3\%

, but yields safer recovery behavior in severe, unseen conditions.

Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman +2

Department of Advanced Computing Sciences, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, Netherlands

Adversarial Training Aerial Robotics UAV Landing on Moving Platforms Quadrotor Control Robust RL Heterogeneous MARL

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Jul 1, 2026

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

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.

Chun-Kit Li, Iok Long Sit, Ming Fung Siu +4

Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR. · Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology, Hong Kong SAR. · Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Hong Kong SAR. +2

RL Control Aerial Robotics Hierarchical RL UAV Landing on Moving Platforms

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Abstract

UAV Landing on Moving Platforms

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