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Stonefish: Supporting Machine Learning Research in Marine Robotics

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arxiv 2502.11887 v2 pith:IOOSEMMF submitted 2025-02-17 cs.RO cs.AIcs.SYeess.SY

Michele Grimaldi , Patryk Cieslak , Eduardo Ochoa , Vibhav Bharti , Hayat Rajani , Ignacio Carlucho , Maria Koskinopoulou , Yvan R. Petillot
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Nuno Gracias
This is my paper · ORCID
classification cs.ROcs.AIcs.SYeess.SY
keywords marineroboticscamerastonefishconditionslearningmachineoperations
verification ladder T0 review T1 audit T2 compute T3 formal
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Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Distributed AI Agents for Cognitive Underwater Robot Autonomy

    cs.RO 2025-07 reject novelty 6.0 of 10

    UROSA controls underwater robots with distributed LLM/VLM agents, retrieval memory, and runtime code generation; feasibility is shown, but the claimed advantage over classical planners is not.

  2. LOTUSim: Multi-Domain Simulator for Marine Robotics

    cs.MA 2026-07 conditional novelty 5.5 of 10

    LOTUSim delivers real-time multi-user HITL simulation of heterogeneous maritime fleets plus an Ekman-layered current model that substantially reduces error versus Gauss–Markov baselines.

  3. PyGemini: Unified Software Development towards Maritime Autonomy Systems

    cs.RO 2025-06 conditional novelty 5.0 of 10

    PyGemini presents a Python-native, ECS-based maritime autonomy framework whose Configuration-Driven Development process uses configuration files as both application specifications and acceptance tests.

  4. A Collaborative Reasoning Framework for Anomaly Diagnostics in Underwater Robotics

    cs.RO 2025-11 reject novelty 4.0 of 10

    Storing operator-validated diagnoses in a vector database and retrieving them during anomaly characterization cuts diagnostic dialog turns by 71% and raises characterization specificity from 2.7 to 4.8 in a BlueROV2 t...

  5. A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming

    cs.RO 2025-07 conditional novelty 3.0 of 10

    A review that maps generative AI to aquaculture tasks, with a marine robotics case study, but the synthesis is weakened by overstated claims and weak citation support.

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