CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
A Brain-Inspired Approach for Probabilistic Estimation and Efficient Planning in Precision Physical Interaction
Xing, Dengpeng1,2; Yang, Yiming1,2; Zhang, Tielin1,2; Xu, Bo1,2
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2022-04-20
Pages15
Corresponding AuthorXing, Dengpeng(dengpeng.xing@ia.ac.cn)
AbstractThis article presents a novel structure of spiking neural networks (SNNs) to simulate the joint function of multiple brain regions in handling precision physical interactions. This task desires efficient movement planning while considering contact prediction and fast radial compensation. Contact prediction demands the cognitive memory of the interaction model, and we novelly propose a double recurrent network to imitate the hippocampus, addressing the spatiotemporal property of the distribution. Radial contact response needs rich spatial information, and we use a cerebellum-inspired module to achieve temporally dynamic prediction. We also use a block-based feedforward network to plan movements, behaving like the prefrontal cortex. These modules are integrated to realize the joint cognitive function of multiple brain regions in prediction, controlling, and planning. We present an appropriate controller and planner to generate teaching signals and provide a feasible network initialization for reinforcement learning, which modifies synapses in accordance with reality. The experimental results demonstrate the validity of the proposed method.
KeywordTask analysis Robots Force Planning Mathematical models Brain modeling Biology Brain-inspired structure precision physical interaction spiking neural networks (SNNs)
DOI10.1109/TCYB.2022.3164750
WOS KeywordCORTEX ; MODEL ; TIME
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[62073324] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404]
Funding OrganizationNational Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000785742200001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification类脑模型与计算
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48381
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorXing, Dengpeng
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xing, Dengpeng,Yang, Yiming,Zhang, Tielin,et al. A Brain-Inspired Approach for Probabilistic Estimation and Efficient Planning in Precision Physical Interaction[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022:15.
APA Xing, Dengpeng,Yang, Yiming,Zhang, Tielin,&Xu, Bo.(2022).A Brain-Inspired Approach for Probabilistic Estimation and Efficient Planning in Precision Physical Interaction.IEEE TRANSACTIONS ON CYBERNETICS,15.
MLA Xing, Dengpeng,et al."A Brain-Inspired Approach for Probabilistic Estimation and Efficient Planning in Precision Physical Interaction".IEEE TRANSACTIONS ON CYBERNETICS (2022):15.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xing, Dengpeng]'s Articles
[Yang, Yiming]'s Articles
[Zhang, Tielin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xing, Dengpeng]'s Articles
[Yang, Yiming]'s Articles
[Zhang, Tielin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xing, Dengpeng]'s Articles
[Yang, Yiming]'s Articles
[Zhang, Tielin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.