基于深度强化学习的用于灵巧抓取的线控软机械手

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS
Kunyu Zhou, Baijin Mao, Yuzhu Zhang, Yaozhen Chen, Yuyaocen Xiang, Zhenping Yu, Hongwei Hao, Wei Tang, Yanwen Li, Houde Liu, Xueqian Wang, Xiaohao Wang, Juntian Qu
{"title":"基于深度强化学习的用于灵巧抓取的线控软机械手","authors":"Kunyu Zhou,&nbsp;Baijin Mao,&nbsp;Yuzhu Zhang,&nbsp;Yaozhen Chen,&nbsp;Yuyaocen Xiang,&nbsp;Zhenping Yu,&nbsp;Hongwei Hao,&nbsp;Wei Tang,&nbsp;Yanwen Li,&nbsp;Houde Liu,&nbsp;Xueqian Wang,&nbsp;Xiaohao Wang,&nbsp;Juntian Qu","doi":"10.1002/aisy.202470046","DOIUrl":null,"url":null,"abstract":"<p><b>Cable-Actuated Soft Manipulator Based on Deep Reinforcement Learning</b>\n </p><p>In article number 2400112, Juntian Qu and co-workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short-term memory) neural networks to control the cable-driven soft manipulator. Multi-scenario and multi-task experiments are carried out based on the soft manipulator, such as precisely placing a 6 mm diameter ball into a 10 mm diameter glass bottle and accurately retrieving a shell from within an L-shaped pipe using the soft manipulator.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470046","citationCount":"0","resultStr":"{\"title\":\"A Cable-Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning\",\"authors\":\"Kunyu Zhou,&nbsp;Baijin Mao,&nbsp;Yuzhu Zhang,&nbsp;Yaozhen Chen,&nbsp;Yuyaocen Xiang,&nbsp;Zhenping Yu,&nbsp;Hongwei Hao,&nbsp;Wei Tang,&nbsp;Yanwen Li,&nbsp;Houde Liu,&nbsp;Xueqian Wang,&nbsp;Xiaohao Wang,&nbsp;Juntian Qu\",\"doi\":\"10.1002/aisy.202470046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Cable-Actuated Soft Manipulator Based on Deep Reinforcement Learning</b>\\n </p><p>In article number 2400112, Juntian Qu and co-workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short-term memory) neural networks to control the cable-driven soft manipulator. Multi-scenario and multi-task experiments are carried out based on the soft manipulator, such as precisely placing a 6 mm diameter ball into a 10 mm diameter glass bottle and accurately retrieving a shell from within an L-shaped pipe using the soft manipulator.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":93858,\"journal\":{\"name\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470046\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202470046\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202470046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

基于深度强化学习的线控软机械手 在编号为 2400112 的文章中,曲俊田及其合作者提出了一种结合 LSTM(长短期记忆)神经网络的改进型 TD3(双延迟深度确定性策略梯度)算法,用于控制线控软机械手。基于软机械手开展了多场景和多任务实验,例如利用软机械手将直径为 6 毫米的球精确放入直径为 10 毫米的玻璃瓶中,以及从 L 型管道中精确取出贝壳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Cable-Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning

A Cable-Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning

Cable-Actuated Soft Manipulator Based on Deep Reinforcement Learning

In article number 2400112, Juntian Qu and co-workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short-term memory) neural networks to control the cable-driven soft manipulator. Multi-scenario and multi-task experiments are carried out based on the soft manipulator, such as precisely placing a 6 mm diameter ball into a 10 mm diameter glass bottle and accurately retrieving a shell from within an L-shaped pipe using the soft manipulator.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
4 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信