发表机构
Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Zhejiang University（中国科学院自动化研究所; 中国科学院大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AquaWorld通过共享地形结构生成水下仿真环境，确保地形、场景、任务和流场一致，包含超1万资产，使训练策略验证成功率提升21%，物理试验成功率95%。
AI 中文摘要
水下机器人仿真需要多样化的环境，其中地形、场景组成、任务和流场保持相互一致。我们提出了AquaWorld，一个通过共享地形结构来保持这些关系的世界生成框架。语言条件化计划生成3D地形和共享的结构参考，在随机变化下指导资产放置、任务定义和入流规格。该框架包含超过10,000个水下兼容资产，通过CFD监督的残差模型预测可重用的地形条件平均流场，并支持常规水下车辆和仿生机器鱼。在24对地形上，结构一致随机化产生的跨因素一致性显著优于独立随机化。在匹配预算的策略训练比较中，结构连贯随机化实现的验证成功率比独立随机化高21%。在单独的物理实验中，一个仿真训练的视觉导航策略在95%的物理水池试验中成功，而无需更新其感知或控制模块。总体而言，AquaWorld提供了一种实用方法，生成多样化的水下环境，同时保留流场仿真和机器人学习所需的结构关系。
英文摘要
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.
Comments7 pages, 6 figures
