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arXiv · 2609.22670

AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation

Bin Peng·Tiandong Zhang·Ruidong Wang·Min Luo·Shuo Wang
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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.

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Bin Peng, Tiandong Zhang, Ruidong Wang, Min Luo, Shuo Wang. 2026-09-19. AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation. https://arxiv.org/abs/2609.22670

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