A Brain-Inspired Approach for Probabilistic Estimation and Efficient Planning in Precision Physical Interaction

doi:10.1109/TCYB.2022.3164750

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Dengpeng 1,2; Yang, Yiming 1,2; Zhang, Tielin 1,2; Xu, Bo 1,2
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2022-04-20
页码	15
关键词	Task analysis Robots Force Planning Mathematical models Brain modeling Biology Brain-inspired structure precision physical interaction spiking neural networks (SNNs)
ISSN号	2168-2267
DOI	10.1109/TCYB.2022.3164750
通讯作者	Xing, Dengpeng(dengpeng.xing@ia.ac.cn)
英文摘要	This 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.
资助项目	National Natural Science Foundation of China[62073324] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404]
WOS关键词	CORTEX ; MODEL ; TIME
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000785742200001
资助机构	National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48380]
专题	数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者	Xing, Dengpeng
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 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.