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

doi:10.1109/TCYB.2022.3164750

	A Brain-Inspired Approach for Probabilistic Estimation and Efficient Planning in Precision Physical Interaction
	Xing, Dengpeng1,2 ; Yang, Yiming 1,2; Zhang, Tielin1,2 ; Xu, Bo1,2
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2022-04-20
页码	15
通讯作者	Xing, Dengpeng(dengpeng.xing@ia.ac.cn)
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.
关键词	Task analysis Robots Force Planning Mathematical models Brain modeling Biology Brain-inspired structure precision physical interaction spiking neural networks (SNNs)
DOI	10.1109/TCYB.2022.3164750
关键词[WOS]	CORTEX ; MODEL ; TIME
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62073324] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404]
项目资助者	National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000785742200001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48381
专题	复杂系统认知与决策实验室_听觉模型与认知计算
通讯作者	Xing, Dengpeng
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.