mlsanityData to 14 Oct 2026

Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture

arXiv 2605.02529 ↗First posted 4 May 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Autonomous surface vessels for floating-waste removal operate under varying hydrodynamics, external disturbances, and challenging water-surface perception. We present a field-validated system that combines camera-based polarimetric perception with a lightweight DRL-based controller for floating-waste detection and capture. Camera detections are converted into water-surface target points and tracked by a controller trained entirely in simulation and deployed directly on a retrofitted ASV platform. Our main contribution is a sim-to-real testing methodology that combines a two-stage simulation protocol with a perception abstraction module designed to mimic real camera behavior, enabling reproducible field trials and explicit evaluation of the sim-to-real gap. We apply this framework in matched simulation and field experiments across 14 disturbance regimes to expose failure modes and evaluate robustness. The results show centimeter-level terminal accuracy and indicate robust control performance under the evaluated perturbation regimes. The main source of degradation is insufficient actuation-model fidelity. We also demonstrate the system in a search-and-capture application using real camera detections in real-world conditions over areas of up to 450~m2. The study distills practical lessons for reliable transfer, including improved actuation-model fidelity, targeted domain randomization, and careful management of latency and timestamps across modules, while highlighting remaining challenges.

Key facts. No citing papers in the map yet (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 22 of its 63 references that are in the map. First posted on arXiv 4 May 2026.

Builds on

22 of 63 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests15 May
  2. 2024Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning3 Dec
  3. 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation11 Jul

Other references · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2023Segment Anythingcited by 8,234
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  4. 2019Object Detection in 20 Years: A Surveycited by 531
  5. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
  6. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 238
  7. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  8. 2023ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robotscited by 152
  9. 2023How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligencecited by 58
  10. 2022A Review of Uncertainty for Deep Reinforcement Learningcited by 40
  11. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 38
  12. 2020Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehiclescited by 11
7 more
  1. 2022Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environmentcited by 11
  2. 2023Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learningcited by 8
  3. 2023Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waterscited by 6
  4. 2024PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objectscited by 3
  5. 2025An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVscited by 3
  6. 2023Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Imagescited by 2
  7. 2025Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environmentscited by 1

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  2. 2026Sim-to-Real RL for ASVs using SysID
  3. 2024Vision-driven UAV River Following: Benchmarking with Safe Reinforcement Learning
  4. 2026Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
  5. 2025Ocean Diviner: A Diffusion-Augmented Reinforcement Learning Framework for AUV Robust Control in Underwater Tasks
  6. 2024Synergistic Reinforcement and Imitation Learning for Vision-driven Autonomous Flight of UAV Along River
  7. 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles
  8. 2025Cross-platform Learning-based Fault Tolerant Surfacing Controller for Underwater Robots