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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 develops a GPU-accelerated parallel simulator for scalable and accurate maritime scenario simulation and designs 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.

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.

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

TL;DR

Sim2Sea develops a GPU-accelerated parallel simulator for scalable and accurate maritime scenario simulation and designs 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.

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.
Paper Structure (16 sections, 6 equations, 6 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 6 equations, 6 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Maritime Vessel Simulator
  4. Autonomous Maritime Navigation
  5. Sim-to-Real for Navigation Tasks
  6. Preliminaries
  7. Methodology
  8. Maritime Vessel Navigation Simulator
  9. Maritime Decision Network
  10. Domain Randomization
  11. Experiments
  12. Parallel Simulation Performance
  13. Environmental Setup
  14. Performance Evaluation in Simulation
  15. Sim-to-Real Deployment
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Sim2Sea overview. Left: Parallel maritime simulator with multiple vessel models and interaction simulations. Middle: Spatiotemporal policy with BEV fusion and VO guided active action masking. Right: Zero-shot onboard deployment enabled by domain randomization.
  • Figure 2: Mini Coastline and Mini Port scenarios. Pink polygons denote land or restricted terrain, green rectangles indicate the departure regions, yellow stars mark the goals, dark circles are static obstacles, and light circles are moving obstacles.
  • Figure 3: Learning performance with and without BEV fusion, active action masking and temporal sequence.
  • Figure 4: Learning performance with and without BEV fusion, active action masking and temporal sequence.
  • Figure 5: Onboard interface and camera views. Left: graphical user interface displaying vessel state, mission progress, and chart overlays. Right: forward or side camera view providing situational awareness for safety monitoring.
  • ...and 1 more figures