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

2026-09-19

AI Summary

为解决水下数据稀缺限制智能体应用的问题，提出AquaCap框架，通过代码作为策略实现无需训练的自主导航与操作。

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.

Problem

• Underwater Embodied Intelligence
• Data Collection
• Vision-Language-Action Models

Innovation

• Training-Free
• Code-as-Policy
• Dual-Layer Agent
• Failure-Aware Memory
• Online Adaptation