AquaWorld: Structure-Consistent Underwater World Generation for Robot Simulation

2026-09-18

AI Summary

AquaWorld通过共享地形结构生成一致的水下环境，用于机器人仿真，解决多样化需求与一致性问题，提高训练成功率。

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.

Problem

• Underwater Robot Simulation
• Environment Consistency
• Terrain Structure
• Flow Simulation
• Robot Learning

Innovation

• structure-consistent
• world generation
• underwater simulation
• shared terrain structure
• language-conditioned plan