AquaCap：一种无需训练、以代码为策略的水下具身智能体

Computer Science > Robotics arXiv:2609.23133 (cs) [Submitted on 19 Sep 2026] Title:AquaCap: A Training-Free Underwater Embodied Agent with Code-as-Policy Authors:Xiaoshi Li, Yule Xu, Chunghiu Kong, Yizhou Zhou, Yang Liu, Hao Yang, Zihao Huang, Yunxiao Shan View a PDF of the paper titled AquaCap: A Training-Free Underwater Embodied Agent with Code-as-Policy, by Xiaoshi Li and 7 other authors View PDF HTML (experimental) 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. Comments: Subjects: Robotics (cs.RO) ACM classes: I.2.9; I.2.11 Cite as: arXiv:2609.23133 [cs.RO] (or arXiv:2609.23133v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.23133 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xiaoshi Li [view email] [v1] Sat, 19 Sep 2026 16:59:41 UTC (3,571 KB) Full-text links: Access Paper: View a PDF of the paper titled AquaCap: A Training-Free Underwater Embodied Agent with Code-as-Policy, by Xiaoshi Li and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)