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 mechanism to ensure safe and efficient exploration. Finally, a targeted domain randomization scheme helps bridge the sim-to-real gap. Simulation results demonstrate that our method achieves faster convergence and safer trajectories than established baselines. In addition, our policy trained purely in simulation successfully transfers zero-shot to a 17-ton unmanned vessel operating in real-world congested waters. These results validate the effectiveness of Sim2Sea in achieving reliable sim-to-real transfer for practical autonomous maritime navigation.

翻译：在拥挤的海事环境中实现自主导航对于广泛的实际应用至关重要。然而，由于复杂的船舶交互和显著的环境不确定性，这仍是一个未解决的挑战。现有方法在实际部署中常常失败，这源于显著的仿真到现实差距，该差距由仿真不精确、态势感知不足以及不安全的探索策略所导致。为解决这些问题，我们提出了 \textbf{Sim2Sea}，一个旨在弥合仿真与现实执行的综合框架。Sim2Sea 在三个关键方面取得进展。首先，我们开发了一个 GPU 加速的并行仿真器，用于可扩展且精确的海事场景仿真。其次，我们设计了一个双流时空策略，该策略处理复杂的动力学和多模态感知，并辅以速度障碍物引导的动作掩蔽机制，以确保安全高效的探索。最后，一种有针对性的领域随机化方案有助于弥合仿真到现实的差距。仿真结果表明，我们的方法比现有基线实现了更快的收敛和更安全的轨迹。此外，我们完全在仿真中训练的策略成功地实现了零样本迁移，应用于在现实世界拥挤水域中运行的 17 吨无人驾驶船舶。这些结果验证了 Sim2Sea 在实现实际自主海事导航的可靠仿真到现实迁移方面的有效性。