WaterVideoQA: ASV-Centric Perception and Rule-Compliant Reasoning via Multi-Modal Agents

Autonomous Surface Vehicles (ASVs) are increasingly expected to operate in ports and harbour environments, where operators may specify navigation targets through language-based descriptions rather than predefined coordinates or fixed target identifiers. However, existing ASV navigation methods mainly execute predefined geometric goals or task-specific objectives and give limited attention to language-grounded target specification. This study proposes Semantically Grounded Navigation (SGNav), a framework that enables an ASV to identify and approach a maritime target from an operator-provided description. SGNav integrates text-guided semantic grounding, harbour-aware candidate filtering, CLIP-based semantic verification, grounded target control-state construction, and Proximal Policy Optimisation-based closed-loop control. It grounds the target description in onboard RGB observations, suppresses visually or semantically irrelevant distractors, and converts the selected target into a compact control-oriented representation for policy execution. Experiments in simulated port environments show that SGNav achieves success rates of $97.0\pm1.2\%$, $92.0\pm1.5\%$, and $90.0\pm1.8\%$ across three representative target-reaching tasks, with semantic target accuracy above $97\%$ and wrong-target rates below $3\%$. SGNav also maintains $97.7$--$98.7\%$ success rates across held-out port layouts. In the Task~3 ablation study, removing harbour-aware filtering or semantic consistency reduces the success rate to $40.4\pm2.6\%$ and $50.4\pm3.1\%$, respectively. These findings demonstrate the importance of semantic grounding, harbour-aware filtering, and semantic verification for reliable language-grounded ASV navigation. These results indicate that the proposed perception-to-control framework can support language-grounded target approach manoeuvres of ASV under the complex port environments.

Fine-grained visual understanding and high-level reasoning in real-world open-water environments remain under-explored due to the lack of dedicated benchmarks. We introduce MARINER, a comprehensive benchmark built under the novel Entity-Environment-Event (3E) paradigm. MARINER contains 16,629 multi-source maritime images with 63 fine-grained vessel categories, diverse adverse environments, and 5 typical dynamic maritime incidents, covering fine-grained classification, object detection, and visual question answering tasks. We conduct extensive evaluations on mainstream Multimodal Large language models (MLLMs) and establish baselines, revealing that even advanced models struggle with fine-grained discrimination and causal reasoning in complex marine scenes. As a dedicated maritime benchmark, MARINER fills the gap of realistic and cognitive-level evaluation for maritime multimodal understanding, and promotes future research on robust vision-language models for open-water applications. Appendix and supplementary materials are available at https://lxixim.github.io/MARINER.

Recent advances in vision-language-action models have stimulated growing interest in underwater embodied intelligence. However, their reliance on large-scale interaction data limits their applicability underwater, where data collection is costly and scarce. To address this challenge, we present AquaCap, a training-free Code-as-Policy framework for autonomous underwater navigation and manipulation. AquaCap employs a dual-layer agent that translates task instructions and environmental observations into condition-aware plans and executable control programs. Structured perception then provides the agent with semantic, geometric, and reliability-aware observations under degraded underwater conditions. A failure-aware memory diagnoses unsuccessful actions and supports closed-loop replanning and code revision. This design enables online adaptation without task-specific training or parameter updates. AquaCap achieves a 66.43% success rate in simulation. Real-world experiments further demonstrate autonomous grasping and object transport with an ROV, including the manipulation of targets displaced by hydrodynamic disturbances.