Underwater environments pose unique challenges for robotic navigation and manipulation. While existing research has primarily focused on task-specific methods, studies on general-purpose intelligence for multi-task execution remain scarce. To address this gap, we propose a unified framework for general-purpose underwater robots that integrates perception and action driven by language instructions. First, we develop a data synthesis pipeline to construct USIM, a simulation-based dataset which comprises over 905K frames from 2275 trajectories, totaling approximately 25 hours of BlueROV2 interactions. Furthermore, we propose U0, a vision-language-action (VLA) model capable of executing various tasks from obstacle-avoidance navigation to three-dimensional mobile manipulation. The model features a convolution-attention-based perception (CAP) module, which incorporates target pose estimation as an auxiliary task to explicitly bolster the model's spatial awareness. For evaluation, we establish a systematic assessment framework and an automated pipeline encompassing both offline metrics and online task execution. Experimental results demonstrate that the USIM dataset significantly empowers existing VLA models to adapt to underwater scenarios. Notably, our U0 model achieves state-of-the-art performance: it reduces the offline mean action prediction error to 0.0359 and achieves an overall online success rate of 43.1%, marking a 5.5% improvement over existing competitive baselines (below 37.6%), with navigation tasks reaching as high as 87.5%. These results validate the feasibility of general-purpose intelligence in underwater robotics, providing a foundation for scalable dataset synthesis and aquatic embodied agents.

翻译：水下环境对机器人导航与操作构成独特挑战。现有研究主要聚焦于特定任务方法，而面向多任务执行的通用智能研究仍较为匮乏。为弥补这一不足，我们提出了一种集成语言指令驱动的感知与动作的通用水下机器人统一框架。首先，我们开发了数据合成流水线，构建了基于仿真的数据集USIM，该数据集包含2275条轨迹的超过90.5万帧图像，总计约25小时的BlueROV2交互数据。此外，我们提出了U0——一种能够执行从避障导航到三维移动操作等各类任务的视觉-语言-动作（VLA）模型。该模型采用基于卷积-注意力的感知（CAP）模块，将目标位姿估计作为辅助任务，以显式增强模型的空间感知能力。在评估方面，我们建立了包含离线指标和在线任务执行的系统性评估框架与自动化流水线。实验结果表明，USIM数据集显著增强了现有VLA模型在水下场景的适应能力。值得注意的是，我们的U0模型实现了最优性能：其离线平均动作预测误差降至0.0359，整体在线成功率高达43.1%，较现有竞争基线（低于37.6%）提升了5.5%，其中导航任务成功率可达87.5%。这些结果验证了通用智能在水下机器人领域的可行性，为可扩展数据集合成与水下具身智能体奠定了重要基础。