Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters
Xinyu Cui, Xuanfa Jin, Xue Yan, Yongcheng Zeng, Luoyang Sun, Siying Wei, Ruizhi Zhang, Jian Zhao, Haifeng Zhang, Jun Wang

TL;DR
Sim2Sea introduces a comprehensive framework combining scalable simulation, advanced policy design, and domain randomization to enable reliable zero-shot sim-to-real transfer for autonomous vessel navigation in congested waters.
Contribution
The paper presents a novel integrated approach with a GPU-accelerated simulator, dual-stream spatiotemporal policy, and targeted domain randomization for maritime navigation.
Findings
Achieves faster convergence and safer trajectories in simulation.
Successfully transfers policies zero-shot to real unmanned vessels.
Demonstrates effective navigation in congested waters with real-world deployment.
Abstract
Autonomous navigation in congested maritime environments is a critical capability for a wide range of real-world applications. However, it remains an unresolved challenge due to complex vessel interactions and significant environmental uncertainties. Existing methods often fail in practical deployment due to a substantial sim-to-real gap, which stems from imprecise simulation, inadequate situational awareness, and unsafe exploration strategies. To address these, we propose \textbf{Sim2Sea}, a comprehensive framework designed to bridge simulation and real-world execution. Sim2Sea advances in three key aspects. First, we develop a GPU-accelerated parallel simulator for scalable and accurate maritime scenario simulation. Second, we design a dual-stream spatiotemporal policy that handles complex dynamics and multi-modal perception, augmented with a velocity-obstacle-guided action masking…
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Taxonomy
TopicsMaritime Navigation and Safety · Multimodal Machine Learning Applications · Reinforcement Learning in Robotics
