| 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
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| Source Publication | IEEE TRANSACTIONS ON CYBERNETICS
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| ISSN | 2168-2267 |
| 2022-04-20 | |
| Pages | 15 |
| Corresponding Author | Xing, Dengpeng(dengpeng.xing@ia.ac.cn) |
| Abstract | 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. |
| Keyword | 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 |
| WOS Keyword | CORTEX ; MODEL ; TIME |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[62073324] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404] |
| Funding Organization | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| WOS Research Area | Automation & Control Systems ; Computer Science |
| WOS Subject | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS ID | WOS:000785742200001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Sub direction classification | 类脑模型与计算 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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 Author | Xing, Dengpeng |
| Affiliation | 1.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 Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute 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. |
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