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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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Peng, B., Zhang, T., Wang, R., Luo, M., & Wang, S. (2026). AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation. https://omanscience.com/en/articles/aquaworld-structure-consistent-underwater-world-generation-for-robot-simulation
MLA 9
Peng, Bin, et al. "AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation." https://omanscience.com/en/articles/aquaworld-structure-consistent-underwater-world-generation-for-robot-simulation.
Chicago (author–date)
Peng, Bin, Tiandong Zhang, Ruidong Wang, Min Luo, and Shuo Wang. 2026. "AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation." https://omanscience.com/en/articles/aquaworld-structure-consistent-underwater-world-generation-for-robot-simulation.
Harvard
Peng, B., Zhang, T., Wang, R., Luo, M. and Wang, S. (2026) 'AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation', Available at: https://omanscience.com/en/articles/aquaworld-structure-consistent-underwater-world-generation-for-robot-simulation.
Vancouver
Peng B, Zhang T, Wang R, Luo M, Wang S. AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation. https://omanscience.com/en/articles/aquaworld-structure-consistent-underwater-world-generation-for-robot-simulation
IEEE
B. Peng, T. Zhang, R. Wang, M. Luo, and S. Wang, "AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation," https://omanscience.com/en/articles/aquaworld-structure-consistent-underwater-world-generation-for-robot-simulation.