A Brain-Inspired Approach for Probabilistic Estimation and Efficient Planning in Precision Physical Interaction
文献类型：期刊论文
| 作者 | Xing, Dengpeng1,2 ; Yang, Yiming1,2; Zhang, Tielin1,2; Xu, Bo1,2
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| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
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| 出版日期 | 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. |
| WOS关键词 | CORTEX ; MODEL ; TIME |
| 资助项目 | National Natural Science Foundation of China[62073324] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000785742200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, 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. |
入库方式： OAI收割
来源：自动化研究所
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