Autonomous underwater robots are increasingly deployed for environmental monitoring, infrastructure inspection, subsea resource exploration, and long-horizon exploration. Yet, despite rapid advances in learning-based planning and control, reliable autonomy in real ocean environments remains fundamentally constrained by tightly coupled physical limits. Hydrodynamic uncertainty, partial observability, bandwidth-limited communication, and energy scarcity are not independent challenges; they interact within the closed perception-planning-control loop and often amplify one another over time. This Review develops a constraint-coupled perspective on underwater embodied intelligence, arguing that planning and control must be understood within tightly coupled sensing, communication, coordination, and resource constraints in real ocean environments. We synthesize recent progress in reinforcement learning, belief-aware planning, hybrid control, multi-robot coordination, and foundation-model integration through this embodied perspective. Across representative application domains, we show how environmental monitoring, inspection, exploration, and cooperative missions expose distinct stress profiles of cross-layer coupling. To unify these observations, we introduce a cross-layer failure taxonomy spanning epistemic, dynamic, and coordination breakdowns, and analyze how errors cascade across autonomy layers under uncertainty. Building on this structure, we outline research directions toward physics-grounded world models, certifiable learning-enabled control, communication-aware coordination, and deployment-aware system design. By internalizing constraint coupling rather than treating it as an external disturbance, underwater embodied intelligence may evolve from performance-driven adaptation toward resilient, scalable, and verifiable autonomy under real ocean conditions.

翻译：自主水下机器人正日益广泛地应用于环境监测、基础设施检查、海底资源勘探及长时程探索任务。然而，尽管基于学习的规划与控制技术发展迅速，真实海洋环境中可靠的自主性从根本上仍受限于紧密耦合的物理约束。水动力不确定性、部分可观测性、带宽受限的通信以及能源稀缺性并非相互独立的挑战；它们在封闭的感知-规划-控制回路内相互作用，并常随时间推移相互放大。本文评述从约束耦合的视角探讨水下具身智能，主张必须在真实海洋环境中紧密耦合的感知、通信、协调与资源约束下理解规划与控制问题。我们通过这一具身视角，综合分析了强化学习、信念感知规划、混合控制、多机器人协调以及基础模型集成等领域的最新进展。通过多个代表性应用领域，我们展示了环境监测、检查、勘探及协同任务如何呈现出跨层耦合的不同压力特征。为统一这些观察，我们提出了一个涵盖认知、动态与协调失效的跨层故障分类法，并分析了不确定性条件下错误如何在自主系统各层级间级联传播。基于此框架，我们展望了面向物理基础的世界模型、可验证的学习赋能控制、通信感知的协调机制以及部署感知的系统设计等研究方向。通过将约束耦合内化而非视作外部干扰，水下具身智能有望从性能驱动的适应，向真实海洋条件下具备韧性、可扩展性与可验证性的自主系统演进。