A Survey of Research on Robotic Brain-inspired Intelligence

被引：0
作者：
Wang, Rui-Dong [1 ,2 ]
Wang, Rui [1 ]
Zhang, Tian-Dong [1 ]
Wang, Shuo [1 ,2 ]
机构：
[1] State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
[2] School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 08期
基金：
中国国家自然科学基金;
关键词：
brain-inspired intelligence; brain-inspired robot; Robot; spiking neural network (SNN);
D O I：
10.16383/j.aas.c230705
中图分类号：
学科分类号：
摘要：
After a long time of research and development, traditional robots have been widely used in many fields of production and life, but they still lack the flexibility, stability and adaptability similar to real organisms in complex and changing environments. As a new type of machine intelligence, brain-inspired intelligence uses computational modeling methods to simulate various characteristics of biological nervous systems and realize the reasoning and decision-making based on all kinds of information. In recent years, it has received extensive attention from the academic community. The main applications of brain-inspired intelligence methods in robot perception, decision-making and control problems are introduced. The related research results are analyzed and summarized. Finally, the main problems of software and hardware are pointed out and future development directions of robotic brain-inspired intelligence are proposed. © 2024 Science Press. All rights reserved.
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收藏
页码：1485 / 1501
页数：16
相关论文
共 95 条
[21]  
Li J X, Li D J, Jiang R H, Xiao R, Tang H J, Tan K C., Vision-action semantic associative learning based on spiking neural networks for cognitive robot, IEEE Computational Intelligence Magazine, 17, 4, (2022)
[22]  
Hussaini S, Milford M, Fischer T., Spiking neural networks for visual place recognition via weighted neuronal assignments, IEEE Robotics and Automation Letters, 7, 2, pp. 4094-4101, (2022)
[23]  
Cao Y Q, Chen Y, Khosla D., Spiking deep convolutional neural networks for energy-efficient object recognition, International Journal of Computer Vision, 113, 1, (2015)
[24]  
Rast A D, Adams S V, Davidson S, Davies S, Hopkins M, Rowley A, Et al., Behavioral learning in a cognitive neuromorphic robot: An integrative approach, IEEE Transactions on Neural Networks and Learning Systems, 29, 12, pp. 6132-6144, (2018)
[25]  
Qiao H, Xi X Y, Li Y L, Wu W, Li F F., Biologically inspired visual model with preliminary cognition and active attention adjustment, IEEE Transactions on Cybernetics, 45, 11, pp. 2612-2624, (2015)
[26]  
Qiao H, Li Y L, Li F F, Xi X Y, Wu W., Biologically inspired model for visual cognition achieving unsupervised episodic and semantic feature learning, IEEE Transactions on Cybernetics, 46, 10, pp. 2335-2347, (2016)
[27]  
Deng B, Fan Y R, Wang J, Yang S M., Reconstruction of a fully paralleled auditory spiking neural network and FPGA implementation, IEEE Transactions on Biomedical Circuits and Systems, 15, 6, pp. 1320-1331, (2021)
[28]  
Liu J D, Perez-Gonzalez D, Rees A, Erwin H, Wermter S., A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation, Neurocomputing, 74, 1−3, pp. 129-139, (2010)
[29]  
Gao B, Zhou Y, Zhang Q T, Zhang S L, Yao P, Xi Y, Et al., Memristor-based analogue computing for brain-inspired sound localization with in situ training, Nature Communications, 13, 1, (2022)
[30]  
Chou T S, Bucci L D, Krichmar J L., Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex, Frontiers in Neurorobotics, 9, (2015)
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