Vision-Based Agile Landing on Turbulent Waters

• URL: http://arxiv.org/abs/2605.23717v1
• Date: Fri, 22 May 2026 14:59:17 GMT
• Title: Vision-Based Agile Landing on Turbulent Waters
• Abstract summary: This paper presents a reinforcement-learning-based approach for autonomous multirotor landing on moving maritime platforms.<n>The proposed method uses multirotor state measurements together with local visual features to predict attitude and thrust commands.<n>We show that it outperforms a state-of-the-art Model Predictive Control baseline under platform motions corresponding to Very Rough'' sea conditions.
• Score: 20.61876675265201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 information. 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 representation.

Related papers

• Hydra: A Navigation World Action Model with Discrete Latent Planning and Continuous Flow-Matching Execution [51.519325778407016]
  World models let robots imagine possible futures, but exploiting this capability for real-time control is bottlenecked by a representation misalignment.<n>We present Hydra, a discrete World Action Model that closes this gap by moving the planner, both the sampler and the evaluator, inside the model.<n> Hydra outperforms state-of-the-art world models in goal-directed planning, while matching or exceeding the closed-loop execution capabilities of leading reactive foundation policies.
  arXiv  Detail & Related papers  (2026-08-29T01:58:19Z)
• Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning [3.8494315501944736]
  We present an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment.<n>The policy is trained in simulation under randomized vessel dynamics and is deployed zero-shot to two real-world platforms without any fine-tuning.<n>In real-world experiments on two different platforms, the adaptive policy outperforms non-adaptive learning-based baselines by up to 58% in position mean absolute error.
  arXiv  Detail & Related papers  (2026-07-02T11:04:21Z)
• WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning [8.852817079320781]
  This paper presents WaveLander, a hierarchical control framework via reinforcement learning (RL) that decouples vertical landing decision-making from low-level flight stabilization.<n>RL 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.<n> Simulation results show that WaveLander achieves robust landing performance and generalizes to unseen disturbance conditions.
  arXiv  Detail & Related papers  (2026-07-01T08:28:15Z)
• Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation [63.11032720821731]
  textitSAGE is a framework that enables agents to learn within a physics-grounded semantic abstraction rather than a photorealistic simulation.<n>We demonstrate that textitSAGE significantly improves planner-assisted embodied navigation, achieving a 53.21% LLM-Match Success Rate on A-EQA.
  arXiv  Detail & Related papers  (2026-05-11T07:34:30Z)
• Koopman Operator Framework for Modeling and Control of Off-Road Vehicle on Deformable Terrain [0.0]
  This work presents a hybrid physics-informed and data-driven modeling framework for predictive control of autonomous off-road vehicles.<n>A Koopman linear system can be constructed using data from simulations of a vehicle moving on deformable terrain.<n>Prediction results demonstrate stable short-horizon accuracy and robustness under mild terrain-height variations.
  arXiv  Detail & Related papers  (2026-03-30T20:11:23Z)
• AutoFly: Vision-Language-Action Model for UAV Autonomous Navigation in the Wild [62.47761809929869]
  Vision-language navigation (VLN) requires intelligent agents to navigate environments by interpreting linguistic instructions alongside visual observations.<n>Current VLN research for unmanned aerial vehicles (UAVs) relies on detailed, pre-specified instructions to guide the UAV along predetermined routes.<n>We propose AutoFly, an end-to-end Vision-Language-Action model for autonomous UAV navigation.
  arXiv  Detail & Related papers  (2026-02-10T11:08:07Z)
• NaviHydra: Controllable Navigation-guided End-to-end Autonomous Driving with Hydra-distillation [6.722962084265695]
  NaviHydra is a controllable navigation-guided end-to-end model distilled from an existing rule-based simulator.<n>Our framework accepts high-level navigation commands as control signals, generating trajectories that align with specified intentions.
  arXiv  Detail & Related papers  (2025-12-11T14:05:18Z)
• Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
  Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
  arXiv  Detail & Related papers  (2025-10-27T03:52:45Z)
• Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
  We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
  arXiv  Detail & Related papers  (2024-06-21T13:48:37Z)
• Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning [2.3439981951927296]
  This research proposes a navigational algorithm which can navigate an inland vessel in a wide variety of complex port scenarios. The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains. Results show that our approach outperforms the commonly used dynamic window approach and a benchmark model-free reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2023-11-17T14:22:40Z)
• A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation [11.525073205608681]
  We present a hierarchical pedestrian behavior model that generates high-level decisions through the use of behavior trees. A full implementation of our work is integrated into GeoScenario Server, a scenario definition and execution engine. Our model is shown to replicate the real-world pedestrians' trajectories with a high degree of fidelity and a decision-making accuracy of 98% or better.
  arXiv  Detail & Related papers  (2022-06-01T02:04:38Z)
• A Simulation Benchmark for Vision-based Autonomous Navigation [3.141160852597485]
  This work introduces a simulator benchmark for vision-based autonomous navigation. The benchmark includes a modular integration of different components of a full autonomous visual navigation stack.
  arXiv  Detail & Related papers  (2022-03-24T12:51:10Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.