Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters

2026-03-04

AI Summary

This work addresses the challenge of deploying navigation policies for autonomous vessels in congested waters, where large sim-to-real gaps, environmental uncertainty, and complex multi-agent interactions hinder effective policy transfer. To bridge this gap, the authors propose Sim2Sea, a framework integrating a GPU-accelerated high-fidelity parallel maritime simulator, a dual-stream spatiotemporal policy network, a velocity obstacle–guided action masking mechanism, and a goal-oriented domain randomization strategy to enable zero-shot sim-to-real policy transfer. Experimental results demonstrate that the proposed approach achieves faster convergence and safer trajectories in simulation and, without any fine-tuning, successfully deploys on a 17-ton autonomous vessel to perform reliable navigation in real-world congested maritime environments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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 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.

Problem

• sim-to-real transfer
• autonomous navigation
• congested waters
• maritime vessel
• domain gap

Innovation

• sim-to-real transfer
• maritime autonomous navigation
• GPU-accelerated simulation
• dual-stream spatiotemporal policy
• velocity-obstacle action masking