Sim-to-Real RL for ASVs using SysID
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
Autonomous Surface Vehicles (ASVs) operating in dynamic marine environments require robust control policies for tasks such as path following and station keeping, making reinforcement learning (RL) a promising alternative to classical controllers. However, existing ASV simulators rarely support parallel environments for RL training. Such existing simulators require accurate hydrodynamic modeling from computational fluid dynamics solvers or towing tank tests for setting hydrodynamic parameters to address the sim-to-real gap. To address these challenges, we present an ASV simulator and accompanying pipeline that enables training policies starting from unknown vehicle dynamics. Our framework uses only a CAD model and brief set of open-water field trajectories for approximating and refining both hydrodynamic and thruster parameters. Real-world deployments on a BlueBoat ASV demonstrate successful zero-shot sim-to-real transfer in path following and station-keeping tasks without prior hydrodynamic and propeller information.
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 15 of its 29 references that are in the map. First posted on arXiv 8 Oct 2026.
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15 of 29 references.
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- 2025Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests15 May
- 2025MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics12 Mar
- 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation11 Jul
Other references · 12
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- 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 76
- 2022skrl: Modular and Flexible Library for Reinforcement Learningcited by 49
- 2019Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Realcited by 42
- 2022Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variablescited by 7
- 2025Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboardingcited by 7
- 2023Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waterscited by 6
- 2021How To Train Your HERONcited by 2
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- 2025ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research
- 2025Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
- 2026Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
- 2024Reinforcement-learning robotic sailboats: simulator and preliminary results
- 2024Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles
- 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles
- 2025Fast Policy Learning for 6-DOF Position Control of Underwater Vehicles
- 2025Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes