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

• URL: http://arxiv.org/abs/2603.04057v1
• Date: Wed, 04 Mar 2026 13:36:33 GMT
• Title: Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters
• Abstract summary: Sim2Sea is a comprehensive framework designed to bridge simulation and real-world execution.<n>We develop a GPU-accelerated parallel simulator for scalable and accurate maritime scenario simulation.<n>Results demonstrate that our method achieves faster convergence and safer trajectories than established baselines.
• Score: 12.551759198727487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• LOTUSim: Multi-Domain Simulator for Marine Robotics [0.49838563301861205]
  LOTUSim is a real-time maritime simulator supporting multi-user interaction across aerial, surface, and underwater robotic systems for coordinated naval-style operations.<n> validation demonstrates robust human-in-the-loop performance, maintaining strict real-time execution and high visual fidelity while scaling to large heterogeneous maritime drone swarms.<n>Results confirm LOTUSim's suitability as a simulation platform for operatorin-the-loop maritime robotics research.
  arXiv  Detail & Related papers  (2026-07-03T08:09:01Z)
• Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation [46.80452332618188]
  Simulation Distillation (SimDist) is a framework that distills structural priors from a simulator into a latent world model.<n>By transferring reward and value models directly from simulation, SimDist provides dense planning signals from raw perception.<n>SimDist substantially outperforms prior methods in data efficiency, stability, and final performance.
  arXiv  Detail & Related papers  (2026-03-16T18:00:23Z)
• ForSim: Stepwise Forward Simulation for Traffic Policy Fine-Tuning [13.668397738433852]
  ForSim is a stepwise closed-loop forward simulation paradigm.<n>It preserves multimodal behavioral diversity while ensuring intra-modality consistency.<n>ForSim operates in conjunction with group-relative optimization to fine-tune traffic policy.
  arXiv  Detail & Related papers  (2026-02-02T10:20:11Z)
• SimScale: Learning to Drive via Real-World Simulation at Scale [45.08991279559151]
  We introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs.<n>Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations.<n>We develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision.
  arXiv  Detail & Related papers  (2025-11-28T17:17:38Z)
• SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer [60.19411648245077]
  We propose SPiDR, short for Sim-to-real via Pessimistic Domain Randomization.<n> SPiDR is a scalable algorithm with provable guarantees for safe sim-to-real transfer.<n>We demonstrate that SPiDR effectively ensures safety despite the sim-to-real gap while maintaining strong performance.
  arXiv  Detail & Related papers  (2025-09-23T05:03:00Z)
• Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions [11.633051537198687]
  IntSim is a strategy that explicitly decouples the driving intentions of surrounding actors from their motion planning.<n>IntSim achieves state-of-the-art performance in simulating realistic safety-critical scenarios.
  arXiv  Detail & Related papers  (2025-03-07T06:59:27Z)
• Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation [62.5805866419814]
  Vid2Sim is a novel framework that bridges the sim2real gap through a scalable and cost-efficient real2sim pipeline for neural 3D scene reconstruction and simulation.<n>Experiments demonstrate that Vid2Sim significantly improves the performance of urban navigation in the digital twins and real world by 31.2% and 68.3% in success rate.
  arXiv  Detail & Related papers  (2025-01-12T03:01:15Z)
• Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning [22.077058792635313]
  Coverage path planning is the problem of finding a path that covers the entire free space of a confined area.<n>We investigate the suitability of continuous-space reinforcement learning for this challenging problem.<n>We show that our approach surpasses the performance of both previous RL-based approaches and highly specialized methods.
  arXiv  Detail & Related papers  (2024-06-07T13:24:19Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Provable Sim-to-real Transfer in Continuous Domain with Partial Observations [39.18274543757048]
  Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment.
  arXiv  Detail & Related papers  (2022-10-27T16:37:52Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.