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arXiv · 2609.23133

AquaCap: A Training-Free Underwater Embodied Agent with Code-as-Policy

Xiaoshi Li·Yule Xu·Chunghiu Kong·Yizhou Zhou·Yang Liu·Hao Yang·Zihao Huang·Yunxiao Shan
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Abstract

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.

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Xiaoshi Li, Yule Xu, Chunghiu Kong, Yizhou Zhou, Yang Liu, Hao Yang, Zihao Huang, Yunxiao Shan. 2026-09-19. AquaCap: A Training-Free Underwater Embodied Agent with Code-as-Policy. https://arxiv.org/abs/2609.23133

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