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摘要: 传统机器人经过长时间的研究和发展, 已经在生产和生活的多个领域得到了广泛的应用, 但在复杂多变的环境中依然缺乏与真实生物类似的灵活性、稳定性和适应能力, 类脑智能作为一种新型的机器智能, 使用计算建模的方法模拟生物神经系统的各类特性, 进而实现对各类信息的推理和决策, 近年来受到了学术界的广泛关注. 鉴于此综述了国内外面向机器人系统的类脑智能研究现状, 并对类脑智能方法在机器人感知、决策和控制三个研究方向的成果进行了整理、归纳和分析, 最后从软硬件层面分别指出了机器人类脑智能目前存在的主要问题和未来的发展方向.Abstract: 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.
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Key words:
- Robot /
- brain-inspired robot /
- brain-inspired intelligence /
- spiking neural network (SNN)
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图 9 基于情感决策的控制时域调整网络
Fig. 9 Control time domain adjustment network based on emotional decision
图 11 基于多脑区协同的机械臂控制器
Fig. 11 Manipulator controller based on collaboration of multiple brain regions
图 14 基于小脑网络的机械臂预测矫正控制器
Fig. 14 Predictive correction controller for manipulator based on cerebellar network
图 15 基于LTC神经元的车辆控制模型[83]
Fig. 15 Predictive correction controller for manipulator based on cerebellar network
表 1 机器人类脑感知方向文献总结
Table 1 Summary of the literature of robotic brain inspired perception
模态 研究团队 传感器种类 网络结构 主要功能 视觉 Rast等[40] 相机
(iCub机器人)仿脑内回路 识别物体并将注意力转移到感兴趣的物体上 Zhou等[20] 激光雷达 (LiDAR)
(文中未确切指明)层式 感知物体的三维轮廓 Ambrosano等[45] 相机
(iCub机器人)仿视网膜回路 机器人视线追踪 Li等[21] 相机 层式 手势识别和意图推断 Qiao等[25] 相机 层式 物体 (人脸)识别 Tang等[42] 距离传感器和RGB相机 多网络融合 SLAM Yoon等[41] 相机 仿脑内回路 SLAM Hussaini等[22] 相机
(文中未确切指明)层式 地点识别
(Place recognition)听觉 Deng等[46] 麦克风 层式 语音分类 Zhe等[4] 麦克风 层式 语音识别 Gao等[28] 麦克风 阵列式 声源定位 Liu等[27] 麦克风 仿脑内回路 声源定位 触觉 Chou等[29] 触觉传感器
(文中未确切指明)仿脑内回路 感知人类的轻抚动作 Zeng等[30] 多传感器融合 仿脑内回路 机器人的“痛觉”感知 张超凡等[31] GelStereo触觉传感器 层式 触觉滑动感知 Liu等[38] 基于晶体管的电子皮肤 仿生物触觉回路 触觉记忆与学习
(以痛觉反射为例)Ali等[32] 压阻式传感器阵列 层式 触觉模态分类 Jiang等[33] 压电传感器 层式 表面粗糙度感知 Lee等[37] 基于压阻器和忆阻器的触觉神经元 库网络
(Reservoir)生物组织硬度感知 Han等[36] 基于摩擦纳米发电器的人工触觉感受器 层式 广范围 (3kPa)触觉感知
(以呼吸状态辨识为例)Kang等[34] 事件驱动触觉传感器
NeuTouch[47]层式 触觉对象识别
触觉滑动检测Wen等[39] 基于压阻器和忆阻器的触觉神经元 层式 触觉感知和识别
(以MNIST分类为例)
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表 2 机器人类脑决策方向文献总结
Table 2 Summary of the literature of robotic brain inspired decision
模型结构 研究团队 模型输入 模型输出 主要功能 循环结构 Rueckert等[55] 某一时刻Agent的状态
(例如空间位置)未来一段时间内的位置规划
(位置序列)有限和无限时间范围的任务规划问题 仿脑内回路 Zhao等[52] 视觉图像信息
(State)动作决策
(无人机的前进、后退、向左、向右)无人机飞行 (穿过窗户)过程决策任务 Daglarli等[54] 视觉信息
听觉信息机器人行为序列
决策奖励信号机器人的类人决策
(情感、注意力、意图等推理)左国玉[49] 关于任务、记忆、 观测Affordance
和标签的词语该物品的Affordance (可以抓取)
或者该物品所需Affordance
的建议 (不可抓取)根据不同的任务选择合适的
物品和抓取位置Robertazzi等[51] 模拟视觉刺激
(方波信号)机器人动作
(向左、向右、保持不动)实现机器人在指定任务需求下的“动作抑制“ Qiao等[58] 状态预测误差
奖励预测误差预测时域调整量 在MB和MF控制之间
切换 (图9)层式 Liu等[57] 传感器感知到的数据 (转换为脉冲序列) 机器人控制量 (左右轮速度) 针对不同的任务需求激活不同的突触
实现控制策略的切换Skorheim等[56] 视觉感知信息
(7×7矩阵)运动决策信息
(3×3矩阵)实现虚拟环境中Agent的觅食决策 Zeng等[48] 环境上下文信息与对其他个
体状态和行为的观测对其他个体未来行为的预测 多智能体协同决策
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表 3 机器人类脑控制方向文献总结
Table 3 Summary of the literature of robotic brain inspired control
机器人类型 研究团队 网络输入 网络输出 主要功能 移动机器人 Sergey等[74]
(2-DoF)超声传感器信息
触觉传感器信息控制信号
(电机驱动量)移动控制
(避障)Wang等[67]
(2-DoF)声纳传感器信息 控制信号
(电机驱动量)移动控制
(避障、追踪、沿墙行走)Bing等[64]
(2-DoF)DVS图像信息 控制信号 (电机转速) 移动控制
(车道保持)Liu等[69]
(4-DoF)超声传感器 控制指令
(左、右、前进)移动控制
(避障)Lu等[63]
(4-DoF)超声传感器感知到的距离信息 控制信号 (转换为
左右轮转速)移动控制
(避障)机械臂 Xing等[7]
(未说明)其他自网络输出的运动参数 关节驱动力矩 对小空间操作机器人
实现精确控制Chen等[85]
(1-DoF)期望角度与传感器信息 控制信号补偿量 提机械臂的适应性和鲁棒性 Igancio等[6]
(6-DoF)轨迹规划器生成的轨迹参数 机械臂控制力矩 通过预测运动指令实现对
指令延迟的鲁棒性Zahra等[86]
(6-DoF)目标运动状态与机械臂内部传感器信息 机械臂驱动量 提高机械臂在操作任务中的
精度和运动协调性Carillo等[76]
(2-DoF)期望机械臂状态与目标信息 肩部与肘部驱动转动的调整量 机械臂运动控制 Zahra等[81]
(6-DoF)关节角速度变化量 机器人状态预测 降低机械臂的运动误差和执行时间 Qiao等[79]
(2-DoF)原始控制信号 控制信号纠正量 提升机械臂的运动精度 仿生机器人 Francisco等[87]
(3-DoF)感知运动信号
(sensory-motor signal)眼球转动量 根据机器人头部转动控制机器人
眼球转动 (前庭眼反射)Naya等[5]
(24-DoF)状态观测量
(State)行动
(Action)考虑能耗成本的步态学习 Lele等[61]
(6-DoF)DVS图像信息 步态选择 移动控制 (捕猎) Espinal等[88]
(12-DoF)- 步态生成 作为CPG生成指定的步态 Jiang等[59]
(未说明)NVS图像信息 (神经形态视觉传感器) 控制信号
(传递给CPG生成对应的步态)移动控制
(目标追踪)- Wilson等[82] 控制信号 控制对象的状态预测 实现一般线性系统的自适应控制 表中DoF (Degree of Freedom)为对应文献中机器人的自由度
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[1] 于振中, 李强, 樊启高. 智能仿生算法在移动机器人路径规划优化中的应用综述. 计算机应用研究, 2019, 36(11): 3210−3219. [2] 蔡灿, 刘璇, 张建华, 张明路. 工业机器人避碰算法研究与展望. 机械设计, 2018, 35(04): 1−7. [3] 黄海丰, 刘培森, 李擎, 于欣波. 协作机器人智能控制与人机交互研究综述. 工程科学学报, 2022, 44(04): 780−791. doi: 10.3321/j.issn.1001-053X.2022.4.bjkjdxxb202204028 [4] Zou Zhe, Zhao Rong, Wu Yujie, et al. A hybrid and scalable brain-inspired robotic platform [J]. Scientific Reports, 2020, 10(1). [5] Naya Katsumi, Kutsuzawa Kyo, Owaki Dai, Hayashibe Mitsuhiro. Spiking Neural Network Discovers Energy-Efficient Hexapod Motion in Deep Reinforcement Learning. Ieee Access, 2021, 9: 150345−150354. doi: 10.1109/ACCESS.2021.3126311 [6] Abadia Ignacio, Naveros Francisco, Ros Eduardo, et al. A cerebellar-based solution to the nondeterministic time delay problem in robotic control [J]. Science Robotics, 2021, 6(58). [7] Xing Dengpeng, Li Jiale, Zhang Tielin, Xu Bo. A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space. Ieee Transactions on Neural Networks and Learning Systems, 2022, 33(5): 2094−2105. doi: 10.1109/TNNLS.2021.3111051 [8] Qiao Hong, Chen Jiahao, Huang Xiao. A Survey of Brain-Inspired Intelligent Robots: Integration of Vision, Decision, Motion Control, and Musculoskeletal Systems. Ieee Transactions on Cybernetics, 2022, 52(10): 11267−11280. doi: 10.1109/TCYB.2021.3071312 [9] Qiao Hong, Wu Yaxiong, Zhong Shanlin, et al. Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application. Machine Intelligence Research, 2023, 20(1): 1−18. doi: 10.1007/s11633-022-1390-8 [10] Qiao Hong, Zhong Shanlin, Chen Ziyu, Wang Hongze. Improving performance of robots using human-inspired approaches: a survey [J]. Science China-Information Sciences, 2022, 65(12). [11] Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Networks, 1997, 10(9): 1659−1671. doi: 10.1016/S0893-6080(97)00011-7 [12] Roy Kaushik, Jaiswal Akhilesh, Panda Priyadarshini. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575(7784): 607−617. doi: 10.1038/s41586-019-1677-2 [13] Neftci Emre O. Data and Power Efficient Intelligence with Neuromorphic Learning Machines. Iscience, 2018, 5: 52−68. doi: 10.1016/j.isci.2018.06.010 [14] Bing Zhenshan, Meschede Claus, Roehrbein Florian, et al. A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks [J]. Frontiers in Neurorobotics, 2018, 12. [15] 朱祥维, 沈丹, 肖凯, et al. 类脑导航的机理、算法、实现与展望 [J]. 航空学报: 1-28. [16] Zheng Hanle, Wu Yujie, Deng Lei, et al. Going Deeper With Directly-Trained Larger Spiking Neural Networks; proceedings of the 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, Electr Network, F 2021 Feb 02-09, 2021 [C]. 2021. [17] Wu Yujie, Deng Lei, Li Guoqi, et al. Direct Training for Spiking Neural Networks: Faster, Larger, Better; proceedings of the 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, HI, F 2019 Jan 27-Feb 01, 2019 [C]. 2019. [18] Wu Yujie, Deng Lei, Li Guoqi, et al. Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks [J]. Frontiers in Neuroscience, 2018, 12. [19] Niu Li-Ye, Wei Ying, Liu Wen-Bo, et al. Research Progress of spiking neural network in image classification: a review. Applied Intelligence, 2023, 53(16): 19466−19490. doi: 10.1007/s10489-023-04553-0 [20] Zhou Shibo, Chen Ying, Li Xiaohua, Sanyal Arindam. Deep SCNN-Based Real-Time Object Detection for Self-Driving Vehicles Using LiDAR Temporal Data. Ieee Access, 2020, 8: 76903−76912. doi: 10.1109/ACCESS.2020.2990416 [21] Li Jiaxin, Li Dengju, Jiang Runhao, et al. Vision-Action Semantic Associative Learning Based on Spiking Neural Networks for Cognitive Robot. Ieee Computational Intelligence Magazine, 2022, 17(4): 27−38. doi: 10.1109/MCI.2022.3199623 [22] Hussaini Somayeh, Milford Michael, Fischer Tobias. Spiking Neural Networks for Visual Place Recognition Via Weighted Neuronal Assignments. Ieee Robotics and Automation Letters, 2022, 7(2): 4094−4101. doi: 10.1109/LRA.2022.3149030 [23] Cao Yongqiang, Chen Yang, Khosla Deepak. Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition. International Journal of Computer Vision, 2015, 113(1): 54−66. doi: 10.1007/s11263-014-0788-3 [24] Rast Alexander D., Adams Samantha V., Davidson Simon, et al. Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach [J]. Ieee Transactions on Neural Networks and Learning Systems, 2018, 29(12): 6132−6144. [25] Qiao Hong, Xi Xuanyang, Li Yinlin, et al. Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment. Ieee Transactions on Cybernetics, 2015, 45(11): 2612−2624. doi: 10.1109/TCYB.2014.2377196 [26] Qiao Hong, Li Yinlin, Li Fengfu, et al. Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning. Ieee Transactions on Cybernetics, 2016, 46(10): 2335−2347. doi: 10.1109/TCYB.2015.2476706 [27] Liu Jindong, Perez-Gonzalez David, Rees Adrian, et al. A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation. Neurocomputing, 2010, 74(1-3): 129−139. doi: 10.1016/j.neucom.2009.10.030 [28] Gao Bin, Zhou Ying, Zhang Qingtian, et al. Memristor-based analogue computing for brain-inspired sound localization with in situ training [J]. Nature Communications, 2022, 13(1). [29] Chou Ting-Shuo, Bucci Liam D., Krichmar Jeffrey L. Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex [J]. Frontiers in Neurorobotics, 2015, 9. [30] Feng Hui, Zeng Yi. A brain-inspired robot pain model based on a spiking neural network [J]. Frontiers in Neurorobotics, 2022, 16. [31] 张超凡, 乔一铭, 曹露, et al. 基于神经形态的触觉滑动感知方法. 浙江大学学报(工学版), 2023, 57(04): 683−692. doi: 10.3785/j.issn.1008-973X.2023.04.005 [32] Dabbous Ali, Ibrahim Ali, Valle Maurizio. Feed-Forward SNN for Touch Modality Prediction; proceedings of the 6th International Conference on System-Integrated Intelligence (SysInt), Genova, ITALY, F 2023 Sep 07-09, 2022 [C]. 2023. [33] Jiang Chunming, Yang Le, Zhang Yilei. A Spiking Neural Network With Spike-Timing-Dependent Plasticity for Surface Roughness Analysis. Ieee Sensors Journal, 2022, 22(1): 438−445. doi: 10.1109/JSEN.2021.3120845 [34] Kang Peng, Banerjee Srutarshi, Chopp Henry, et al. Boost event-driven tactile learning with location spiking neurons [J]. Frontiers in Neuroscience, 2023, 17. [35] Navaraj William, Dahiya Ravinder. Fingerprint-Enhanced Capacitive-Piezoelectric Flexible Sensing Skin to Discriminate Static and Dynamic Tactile Stimuli [J]. Advanced Intelligent Systems, 2019, 1(7). [36] Han Joon-Kyu, Tcho Il-Woong, Jeon Seung-Bae, et al. Self-Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System [J]. Advanced Science, 2022, 9(9). [37] Lee Junseok, Kim Seonjeong, Park Seongjin, et al. An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis [J]. Advanced Materials, 2022, 34(24). [38] Liu F. Y., Deswal S., Christou A., et al. Printed synaptic transistor-based electronic skin for robots to feel and learn [J]. Science Robotics, 2022, 7(67). [39] Wen Juan, Zhang Le, Wang Yu-Zhe, Guo Xin. Artificial Tactile Perception System Based on Spiking Tactile Neurons and Spiking Neural Networks [J]. ACS applied materials & interfaces, 2023. [40] Kreiser Raphaela, Waibel Gabriel, Armengol Nuria, et al. Error estimation and correction in a spiking neural network for map formation in neuromorphic hardware; proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Electr Network, F 2020 May 31-Jun 15, 2020 [C]. 2020. [41] Yoon Jong-Hyeok, Raychowdhury Arijit. NeuroSLAM: A 65-nm 7.25-to-8.79-TOPS/W Mixed-Signal Oscillator-Based SLAM Accelerator for Edge Robotics. Ieee Journal of Solid-State Circuits, 2021, 56(1): 66−78. doi: 10.1109/JSSC.2020.3028298 [42] Tang Guangzhi, Shah Arpit, Michmizos Konstantinos P., Ieee. Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM; proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, PEOPLES R CHINA, F 2019 Nov 04-08, 2019 [C]. 2019. [43] Wang Huan, Li Yan-Fu. Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments [J]. Reliability Engineering & System Safety, 2023, 233. [44] Lopez-Randulfe Javier, Duswald Tobias, Bing Zhenshan, Knoll Alois. Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar [J]. Frontiers in Neurorobotics, 2021, 15. [45] Ambrosano Alessandro, Vannucci Lorenzo, Albanese Ugo, et al. Retina Color-Opponency Based Pursuit Implemented Through Spiking Neural Networks in the Neurorobotics Platform; proceedings of the 5th International Conference on Biomimetic and Biohybrid Systems (Living Machines), Edinburgh, SCOTLAND, F 2016 Jul 19-22, 2016 [C]. 2016. [46] Deng Bin, Fan Yanrong, Wang Jiang, Yang Shuangming. Auditory perception architecture with spiking neural network and implementation on FPGA. Neural Networks, 2023, 165: 31−42. doi: 10.1016/j.neunet.2023.05.026 [47] Taunyazov Tasbolat, Sng Weicong, See Hian Hian, et al. Event-Driven Visual-Tactile Sensing and Learning for Robots; proceedings of the 16th Conference on Robotics - Science and Systems (RSS), Electr Network, F 2020 Jul 12-16, 2020 [C]. 2020. [48] Zhao Zhuoya, Zhao Feifei, Zhao Yuxuan, et al. A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition. Patterns (New York, NY), 2023, 4(8): 100775−100775. [49] 左国玉, 刘洪星, 龚道雄, 阮晓钢. 受脑启发的机器人认知抓取决策模型. 北京工业大学学报, 2021, 47(08): 863−873. doi: 10.11936/bjutxb2020120034 [50] Sun Tianjun, Gao Zhenhai, Chang Zhiyong, Zhao Kehan. Brain-like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car-following. Journal of Bionic Engineering, 2021, 18(6): 1439−1451. doi: 10.1007/s42235-021-00113-9 [51] Robertazzi Federica, Vissani Matteo, Schillaci Guido, Falotico Egidio. Brain-inspired meta-reinforcement learning cognitive control in conflictual inhibition decision-making task for artificial agents. Neural Networks, 2022, 154: 283−302. doi: 10.1016/j.neunet.2022.06.020 [52] Zhao Feifei, Zeng Yi, Wang Guixiang, et al. A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations. Cognitive Computation, 2018, 10(2): 296−306. doi: 10.1007/s12559-017-9511-3 [53] Zhao Feifei, Zeng Yi, Xu Bo. A Brain-Inspired Decision-Making Spiking Neural Network and Its Application in Unmanned Aerial Vehicle [J]. Frontiers in Neurorobotics, 2018, 12. [54] Daglarli Evren. Computational Modeling of Prefrontal Cortex for Meta-Cognition of a Humanoid Robot. Ieee Access, 2020, 8: 98491−98507. doi: 10.1109/ACCESS.2020.2998396 [55] Rueckert Elmar, Kappel David, Tanneberg Daniel, et al. Recurrent Spiking Networks Solve Planning Tasks [J]. Scientific Reports, 2016, 6. [56] Skorheim Steven, Lonjers Peter, Bazhenov Maxim. A Spiking Network Model of Decision Making Employing Rewarded STDP [J]. Plos One, 2014, 9(3). [57] Liu Junxiu, Lu Hao, Luo Yuling, Yang Su. Spiking neural network-based multi-task autonomous learning for mobile robots [J]. Engineering Applications of Artificial Intelligence, 2021, 104. [58] Huang Xiao, Wu Wei, Qiao Hong. Connecting Model-Based and Model-Free Control With Emotion Modulation in Learning Systems. Ieee Transactions on Systems Man Cybernetics-Systems, 2021, 51(8): 4624−4638. doi: 10.1109/TSMC.2019.2933152 [59] Jiang Zhuangyi, Bing Zhenshan, Huang Kai, Knoll Alois. Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot [J]. Frontiers in Neurorobotics, 2019, 13. [60] Wang Ming, Zhang Yiyang, Yu Junzhi. An SNN-CPG Hybrid Locomotion Control for Biomimetic Robotic Fish [J]. Journal of Intelligent & Robotic Systems, 2022, 105(2). [61] Lele Ashwin, Fang Yan, Ting Justin, Raychowdhury Arijit. An End-to-End Spiking Neural Network Platform for Edge Robotics: From Event-Cameras to Central Pattern Generation. Ieee Transactions on Cognitive and Developmental Systems, 2022, 14(3): 1092−1103. doi: 10.1109/TCDS.2021.3097675 [62] Cao Zhiqiang, Cheng Long, Zhou Chao, et al. Spiking neural network-based target tracking control for autonomous mobile robots. Neural Computing & Applications, 2015, 26(8): 1839−1847. [63] Lu Hao, Liu Junxiu, Luo Yuling, et al. An autonomous learning mobile robot using biological reward modulate STDP. Neurocomputing, 2021, 458: 308−318. doi: 10.1016/j.neucom.2021.06.027 [64] Bing Zhenshan, Meschede Claus, Chen Guang, et al. Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle. Neural Networks, 2020, 121: 21−36. doi: 10.1016/j.neunet.2019.05.019 [65] Bing Zhenshan, Baumann Ivan, Jiang Zhuangyi, et al. Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle [J]. Frontiers in Neurorobotics, 2019, 13. [66] Nichols Eric, McDaid L. J., Siddique N. H. CASE STUDY ON A SELF-ORGANIZING SPIKING NEURAL NETWORK FOR ROBOT NAVIGATION [J]. International Journal of Neural Systems, 2010, 20(6): 501-508. [67] Wang Xiuqing, Hou Zeng-Guang, Lv Feng, et al. Mobile robots' modular navigation controller using spiking neural networks. Neurocomputing, 2014, 134: 230−238. doi: 10.1016/j.neucom.2013.07.055 [68] Bing Zhenshan, Jiang Zhuangyi, Cheng Long, et al. End to End Learning of a Multi-layered SNN Based on R-STDP for a Target Tracking Snake-like Robot; proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, CANADA, F 2019 May 20-24, 2019 [C]. 2019. [69] Liu Junxiu, Hua Yifan, Yang Rixing, et al. Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance [J]. Frontiers in Neuroscience, 2022, 16. [70] Jose Guerrero-Criollo Roberto, Alejandro Castano-Lopez Jason, Hurtado-Lopez Julian, Fernando Ramirez-Moreno David. Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot [J]. Frontiers in Neurorobotics, 2023, 17. [71] Jose Guerrero-Criollo Roberto, Alejandro Castano-Lopez Jason, Ernesto Diaz-Cuchala Raul, et al. Design and simulation of a bio-inspired neural network for the motor control of a mobile automaton; proceedings of the IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), Cali, COLOMBIA, F 2022 Jul 27-29, 2022 [C]. 2022. [72] Abadia Ignacio, Naveros Francisco, Garrido Jesus A., et al. On Robot Compliance: A Cerebellar Control Approach. Ieee Transactions on Cybernetics, 2021, 51(5): 2476−2489. doi: 10.1109/TCYB.2019.2945498 [73] 戴嘉伟, 熊智, 晁丽君, 杨闯. 基于STDP奖励调节的类脑面向目标导航. 导航定位与授时, 2023, 10(02): 47−56. [74] Lobov Sergey A., Mikhaylov Alexey N., Shamshin Maxim, et al. Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot [J]. Frontiers in Neuroscience, 2020, 14. [75] Sathyanesan Aaron, Zhou Joy, Scafidi Joseph, et al. Emerging connections between cerebellar development, behaviour and complex brain disorders. Nature Reviews Neuroscience, 2019, 20(5): 298−313. doi: 10.1038/s41583-019-0152-2 [76] Carrillo Richard R., Ros Eduardo, Boucheny Christian, Coenen Olivier J. M. D. A real-time spiking cerebellum model for learning robot control. Biosystems, 2008, 94(1-2): 18−27. doi: 10.1016/j.biosystems.2008.05.008 [77] Oikonomou Katerina Maria, Kansizoglou Ioannis, Gasteratos Antonios. A Hybrid Spiking Neural Network Reinforcement Learning Agent for Energy-Efficient Object Manipulation [J]. Machines, 2023, 11(2). [78] Liu Yuxiang, Pan Wei. Spiking Neural-Networks-Based Data-Driven Control [J]. Electronics, 2023, 12(2). [79] Zhang J., Chen J., Wu W., Qiao H. A Cerebellum-Inspired Prediction and Correction Model for Motion Control of a Musculoskeletal Robot [J]. IEEE Transactions on Cognitive and Developmental Systems, 2023, 15(3): 1209−1223. [80] Cao Yu, Huang Jian, Ding Gangzheng, Wang Yongji. Design of Nonlinear Predictive Control for Pneumatic Muscle Actuator Based on Echo State Gaussian Process; proceedings of the 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), Toulouse, FRANCE, F 2017 Jul 09-14, 2017 [C]. 2017. [81] Zahra Omar, Navarro-Alarcon David, Tolu Silvia. Vision-Based Control for Robots by a Fully Spiking Neural System Relying on Cerebellar Predictive Learning [J/OL] 2020, arXiv: 2011.01641 [https://ui.adsabs.harvard.edu/abs/2020arXiv201101641Z. 10.48550/arXiv.2011.01641 [82] Wilson Emma D., Assaf Tareq, Pearson Martin J., et al. Biohybrid control of general linear systems using the adaptive filter model of cerebellum [J]. Frontiers in Neurorobotics, 2015, 9. [83] Lechner Mathias, Hasani Ramin, Amini Alexander, et al. Neural circuit policies enabling auditable autonomy. Nature Machine Intelligence, 2020, 2(10): 642−652. doi: 10.1038/s42256-020-00237-3 [84] Hasani Ramin, Lechner Mathias, Amini Alexander, et al. Closed-form continuous-time neural networks. Nature Machine Intelligence, 2022, 4(11): 992−1003. doi: 10.1038/s42256-022-00556-7 [85] Chen Xinyi, Zhu Wenxin, Liang Wenyu, et al. Control of Antagonistic McKibben Muscles via a Bio-inspired Approach. Journal of Bionic Engineering, 2022, 19(6): 1771−1789. doi: 10.1007/s42235-022-00225-w [86] Zahra Omar, Navarro-Alarcon David, Tolu Silvia. A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks [J]. International Journal of Neural Systems, 2022, 32(08). [87] Naveros Francisco, Luque Niceto R., Ros Eduardo, Arleo Angelo. VOR Adaptation on a Humanoid iCub Robot Using a Spiking Cerebellar Model. Ieee Transactions on Cybernetics, 2020, 50(11): 4744−4757. doi: 10.1109/TCYB.2019.2899246 [88] Espinal A., Rostro-Gonzalez H., Carpio M., et al. Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation [J]. Computational Intelligence and Neuroscience, 2016, 2016. [89] Aitsam Muhammad, Davies Sergio, Di Nuovo Alessandro. Neuromorphic Computing for Interactive Robotics: A Systematic Review. IEEE ACCESS, 2022, 10: 122261−122279. doi: 10.1109/ACCESS.2022.3219440 [90] Guo Yufei, Huang Xuhui, Ma Zhe. Direct learning-based deep spiking neural networks: a review [J]. FRONTIERS IN NEUROSCIENCE, 2023, 17. [91] Shen Guobin, Zhao Dongcheng, Dong Yiting, Zeng Yi. Brain-inspired neural circuit evolution for spiking neural networks. Proceedings of the National Academy of Sciences of the United States of America, 2023, 120(39): e2218173120−e2218173120. [92] Javanshir Amirhossein, Thanh Thi Nguyen, Mahmud M. A. Parvez, Kouzani Abbas Z. Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks. NEURAL COMPUTATION, 2022, 34(6): 1289−1328. doi: 10.1162/neco_a_01499 [93] Quintana Fernando M., Perez-Pena Fernando, Galindo Pedro L. Bio-plausible digital implementation of a reward modulated STDP synapse. NEURAL COMPUTING & APPLICATIONS, 2022, 34(18): 15649−15660. [94] 胡金岭. 机械臂类脑控制平台的设计与实现研究 [D]; 天津大学, 2022. -
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