Learning Agile Swimming: An End-to-End Approach without CPGs
2025-02
发表期刊ARXIV
ISSN2377-3774
EISSN2377-3766
卷号10期号:2页码:1992-1999
发表状态已发表
DOIarXiv:2409.10019
摘要

The pursuit of agile and efficient underwater robots, especially bio-mimetic robotic fish, has been impeded by challenges in creating motion controllers that are able to fully exploit their hydrodynamic capabilities. This paper addresses these challenges by introducing a novel, model-free, end-to-end control framework that leverages Deep Reinforcement Learning (DRL) to enable agile and energy-efficient swimming of robotic fish. Unlike existing methods that rely on predefined trigonometric swimming patterns like Central Pattern Generators (CPG), our approach directly outputs low-level actuator commands without strong constraint, enabling the robotic fish to learn agile swimming behaviors. In addition, by integrating a highperformance Computational Fluid Dynamics (CFD) simulator with innovative sim-to-real strategies, such as normalized density matching and servo response matching, the proposed framework significantly mitigates the sim-to-real gap, facilitating direct transfer of control policies to real-world environments without fine-tuning. Comparative experiments demonstrate that our method achieves faster swimming speeds, smaller turning radii, and reduced energy consumption compared to the conventional CPG-PID-based controllers. Furthermore, the proposed framework shows promise in addressing complex tasks in diverse scenario, paving the way for more effective deployment of robotic fish in real aquatic environments.

关键词Robotic fish deep reinforcement learning computational fluid dynamics sim-to-real
URL查看原文
收录类别EI
语种英语
资助项目National Natural Science Foundation of China[62072310]
WOS类目Computer Science, Artificial Intelligence
WOS记录号PPRN:119222069
出版者Institute of Electrical and Electronics Engineers Inc.
EI入藏号20250317714762
EI主题词Reinforcement learning
EI分类号101.6.1 Robotic Assistants - 1101.2 Machine Learning - 1101.2.1 Deep Learning - 301.1 Fluid Flow - 301.1.3 Aerodynamics (fluid flow) - 651 Aerodynamics - 731.5 Robotics - 731.6 Robot Applications
原始文献类型Journal article (JA)
来源库IEEE
文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/464725
专题信息科学与技术学院
信息科学与技术学院_PI研究组_刘晓培组
信息科学与技术学院_博士生
信息科学与技术学院_PI研究组_汪阳组
通讯作者Wang, Yang
作者单位
ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
第一作者单位信息科学与技术学院
通讯作者单位信息科学与技术学院
第一作者的第一单位信息科学与技术学院
推荐引用方式
GB/T 7714
Lin, Xiaozhu,Liu, Xiaopei,Wang, Yang. Learning Agile Swimming: An End-to-End Approach without CPGs[J]. ARXIV,2025,10(2):1992-1999.
APA Lin, Xiaozhu,Liu, Xiaopei,&Wang, Yang.(2025).Learning Agile Swimming: An End-to-End Approach without CPGs.ARXIV,10(2),1992-1999.
MLA Lin, Xiaozhu,et al."Learning Agile Swimming: An End-to-End Approach without CPGs".ARXIV 10.2(2025):1992-1999.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[Lin, Xiaozhu]的文章
[Liu, Xiaopei]的文章
[Wang, Yang]的文章
百度学术
百度学术中相似的文章
[Lin, Xiaozhu]的文章
[Liu, Xiaopei]的文章
[Wang, Yang]的文章
必应学术
必应学术中相似的文章
[Lin, Xiaozhu]的文章
[Liu, Xiaopei]的文章
[Wang, Yang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。