Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests

2025/05/15 by Luis Felipe Wolf Batista, Stéphanie Aravecchia, Batista, Luis F. W. +5
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Maritime Navigation and Safety #Reinforcement Learning in Robotics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2505.10033

openalex publication_date 2025/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected disturbances such as asymmetric drag and an off-center payload. We assess the agent's performance under these perturbations in both simulation and real-world experiments, quantifying performance degradation and benchmarking it against an MPC baseline. Results indicate that the DRL agent performs reliably despite significant disturbances. Along with the open-source release of our implementation, we provide insights into effective training strategies, real-world challenges, and practical considerations for deploying DRLbased ASV controllers.

Citations

• A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation
• OtterROS: Picking and Programming an Uncrewed Surface Vessel for Experimental Field Robotics Research with ROS 2
• RANS: Highly-Parallelised Simulator for Reinforcement Learning based Autonomous Navigating Spacecrafts
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• Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment
• Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
• Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles
• Proximal Policy Optimization Algorithms
• Handbook of Marine Craft Hydrodynamics and Motion Control

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