[
    {
        "id": "osp-26329",
        "type": "article-journal",
        "title": "AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation",
        "author": [
            {
                "family": "Peng",
                "given": "Bin"
            },
            {
                "family": "Zhang",
                "given": "Tiandong"
            },
            {
                "family": "Wang",
                "given": "Ruidong"
            },
            {
                "family": "Luo",
                "given": "Min"
            },
            {
                "family": "Wang",
                "given": "Shuo"
            }
        ],
        "URL": "https://omanscience.com/en/articles/aquaworld-structure-consistent-underwater-world-generation-for-robot-simulation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Underwater robot simulation requires diverse environments in which terrain, scene composition, tasks, and currents remain mutually consistent. We present AquaWorld, a world-generation framework that preserves these relationships through shared terrain structure. A language-conditioned plan generates 3D terrain and shared structural references that guide asset placement, task definition, and inflow specification under stochastic variation. The framework incorporates over 10,000 underwater-compatible assets, predicts reusable terrain-conditioned mean-flow fields through a CFD-supervised residual model, and supports conventional underwater vehicles and bio-inspired robotic fish. On 24 paired terrains, structure-consistent randomization produces substantially better cross-factor consistency than independent randomization. In a matched-budget policy-training comparison, structurally coherent randomization achieves a validation success rate 21% higher than independent randomization. In separate physical experiments, a simulation-trained visual navigation policy succeeds in 95% of physical tank trials without updating its perception or control modules. Overall, AquaWorld provides a practical way to generate varied underwater environments while retaining the structural relationships needed for flow simulation and robot learning."
    }
]