ARXIV:2603.04057 · AUTONOMOUS MARITIME NAVIGATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters. However, it remains an unresolved challenge due to complex vessel interactions and significant environmental uncertainties.

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Simulation results demonstrate that our method achieves faster convergence and safer trajectories than established baselines.

WHY NOW

Autonomous Maritime Navigation moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainSim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.04057 · AUTONOMOUS MARITIME NAVIGATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Autonomous Maritime Navigation moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainSim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.04057

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

Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback24 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Autonomous Maritime Navigation moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters. However, it remains an unresolved challenge due to complex vessel interactions and significant environmental uncertainties.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Simulation results demonstrate that our method achieves faster convergence and safer trajectories than established baselines.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Autonomous Maritime Navigation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Autonomous Maritime Navigation

Competitors

not indexed

Competitive landscape

Sim2Sea enables safe and efficient sim-to-real transfer for autonomous maritime vessel navigation in congested waters.

Segment

Autonomous Maritime Navigation

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.04057 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(24)

Robust Collision Avoidance for ASVs Using Deep Reinforcement Learning with Sim2Real Methods in Static Obstacle Environments

2025Changgyu Han, Se-Kil Park et al.

AquaNav - Unmanned Surface Vehicle Simulator Using Unreal Engine 5

2025Pruthviraj Mane, A. George et al.

Partially Observed Markov Decision Processes

2025V. Krishnamurthy

Advancing Maritime Route Optimization: Using Reinforcement Learning for Ensuring Safety and Fuel Efficiency

2024Jisoo Kim, Byeonggong Hwang et al.

Adaptive collision avoidance decision system for autonomous ship navigation

2024Xingya Zhao, Yixiong He et al.

A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation

2024L. F. W. Batista, Junghwan Ro et al.

UUVSim: Intelligent Modular Simulation Platform for Unmanned Underwater Vehicle Learning

2024Zekai Zhang, Jingzehua Xu et al.

Learning to Navigate Efficiently and Precisely in Real Environments

2024G. Bono, Hervé Poirier et al.

NeuronsGym: A Hybrid Framework and Benchmark for Robot Tasks with Sim2Real Policy Learning

2023Haoran Li, Shasha Liu et al.

Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation

2023Chengbo Wang, Xinyu Zhang et al.

DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles

2023Zhanteng Xie, P. Dames

COLREGs and their application in collision avoidance algorithms: A critical analysis

2022J. A. García Maza, R. P. Argüelles

DAVE Aquatic Virtual Environment: Toward a General Underwater Robotics Simulator

2022Mabel M. Zhang, Woen-Sug Choi et al.

A Novel Reinforcement Learning Collision Avoidance Algorithm for USVs Based on Maneuvering Characteristics and COLREGs

2022Yunsheng Fan, Zhe Sun et al.

Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey

2021Xinyu Zhang, Chengbo Wang et al.

COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning

2020Eivind Meyer, Amalie Heiberg et al.

COLREGs-compliant multiship collision avoidance based on deep reinforcement learning

2019Luman Zhao, M. Roh

A simple technique for identifying vessel model parameters

2018V. Golikov, V. Golikov et al.

Deep Reinforcement Learning: An Overview

2017Yuxi Li

Introduction of MMG standard method for ship maneuvering predictions

2015H. Yasukawa, Y. Yoshimura

Showing 20 of 24 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkSim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application

7.0

Prior WorkFrom Vessel Trajectories to Safety-Critical Encounter Scenarios: A Generative AI Framework for Autonomous Ship Digital Testing

7.0

Prior WorkSEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments

7.0

Prior WorkSIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

7.0

Prior WorkSynthetic-to-Real Domain Bridging for Single-View 3D Reconstruction of Ships for Maritime Monitoring

7.0

Prior WorkCritic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids

7.0

Extension

Builds On ThisSim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation

4.0

Builds On ThisTransferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment

3.0

Commercially relevant

Higher ViabilityReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation with Moving Obstacles

8.0

Higher ViabilitySysNav: Multi-Level Systematic Cooperation Enables Real-World, Cross-Embodiment Object Navigation

8.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.04057
Route: /paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters
Active tab: read
Artifact: sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters/paper-pack
REST build passport API/api/v1/paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters",
  "endpoints": {
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    "build_passport": "/api/v1/paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters/build-passport",
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

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

Canonical ID sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters | Route /paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters

MCP example

{
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  }
}

source_context

{
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  "route": "/paper/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters",
  "paper_ref": "sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

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

/buildability/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters

Watchwatch

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

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters

Paper ref

sim2sea-sim-to-real-policy-transfer-for-maritime-vessel-navigation-in-congested-waters

arXiv id

2603.04057

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

ecc21990cbc48269aab136f29bbe2fc3999fd30b4d2aecc52d224d06983c06e3

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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

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

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Claim map

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Brief

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline