将视觉运动协调的深度学习与物体识别相结合,实现机器人物体拾取的高级接口

Manfred Eppe, Matthias Kerzel, Sascha S. Griffiths, Hwei Geok Ng, S. Wermter
{"title":"将视觉运动协调的深度学习与物体识别相结合,实现机器人物体拾取的高级接口","authors":"Manfred Eppe, Matthias Kerzel, Sascha S. Griffiths, Hwei Geok Ng, S. Wermter","doi":"10.1109/HUMANOIDS.2017.8246935","DOIUrl":null,"url":null,"abstract":"We present a proof of concept to show how a deep network for end-to-end visuomotor learning to grasp is coupled with an attention focus mechanism for state-of-the-art object detection with convolutional neural networks. The cognitively motivated integration of both methods in a single robotic system allows us to realize a high-level interface to use the visuomotor network in environments with several objects, which otherwise would only be usable in environments with a single object. The resulting system is deployed on a humanoid robot, and we perform several real-world grasping experiments that demonstrate the feasibility of our approach.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Combining deep learning for visuomotor coordination with object identification to realize a high-level interface for robot object-picking\",\"authors\":\"Manfred Eppe, Matthias Kerzel, Sascha S. Griffiths, Hwei Geok Ng, S. Wermter\",\"doi\":\"10.1109/HUMANOIDS.2017.8246935\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a proof of concept to show how a deep network for end-to-end visuomotor learning to grasp is coupled with an attention focus mechanism for state-of-the-art object detection with convolutional neural networks. The cognitively motivated integration of both methods in a single robotic system allows us to realize a high-level interface to use the visuomotor network in environments with several objects, which otherwise would only be usable in environments with a single object. The resulting system is deployed on a humanoid robot, and we perform several real-world grasping experiments that demonstrate the feasibility of our approach.\",\"PeriodicalId\":143992,\"journal\":{\"name\":\"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HUMANOIDS.2017.8246935\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HUMANOIDS.2017.8246935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

我们提出了一个概念证明,展示了端到端视觉运动学习的深度网络如何与卷积神经网络中用于最先进目标检测的注意力聚焦机制相结合。在一个机器人系统中,两种方法的认知动机集成使我们能够实现一个高级接口,在具有多个对象的环境中使用视觉运动网络,否则只能在具有单个对象的环境中使用。最终的系统部署在人形机器人上,我们进行了几个真实世界的抓取实验,证明了我们方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining deep learning for visuomotor coordination with object identification to realize a high-level interface for robot object-picking
We present a proof of concept to show how a deep network for end-to-end visuomotor learning to grasp is coupled with an attention focus mechanism for state-of-the-art object detection with convolutional neural networks. The cognitively motivated integration of both methods in a single robotic system allows us to realize a high-level interface to use the visuomotor network in environments with several objects, which otherwise would only be usable in environments with a single object. The resulting system is deployed on a humanoid robot, and we perform several real-world grasping experiments that demonstrate the feasibility of our approach.
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
自引率
0.00%
发文量
0
×
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