Embodied tactile perception and learning

Huaping Liu, Di Guo, F. Sun, Wuqiang Yang, S. Furber, Teng Sun
{"title":"Embodied tactile perception and learning","authors":"Huaping Liu, Di Guo, F. Sun, Wuqiang Yang, S. Furber, Teng Sun","doi":"10.26599/BSA.2020.9050012","DOIUrl":null,"url":null,"abstract":"Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.26599/BSA.2020.9050012","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Science Advances","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.26599/BSA.2020.9050012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
体现触觉感知和学习
各种生物都表现出体现智能,这是通过大脑、身体和环境的协同相互作用来反映的。实施体智能的实际行为是主体与环境通过信息感知和物理操纵进行持续的动态交互而产生的。机器人与环境之间的物理交互是实现具身感知和学习的基础。触觉信息在这种物理交互过程中起着至关重要的作用。它可以用于确保安全性、稳定性和依从性,并且可以提供使用其他感知模式难以捕获的独特信息。然而,由于现有传感器以及感知和学习方法的限制,机器人触觉研究的发展明显滞后于视觉和听觉等其他感知方式,严重制约了机器人实施体智能的发展。介绍了目前机器人触觉体现智能研究面临的挑战,综述了机器人触觉体现智能的理论和方法。基于新型大规模触觉阵列传感装置的发展,可以设计触觉感知和学习方法,以突破触觉智能的神经形态计算技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
27
审稿时长
10 weeks
期刊最新文献
A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022 Overview of recognition methods for SSVEP-based BCIs in World Robot Contest 2022: MATLAB undergraduate group Algorithm contest of motor imagery BCI in the World Robot Contest 2022: A survey Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022: BCI Turing Test Overview of the winning approaches in 2022 World Robot Contest Championship–Asynchronous SSVEP
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1