Mainly engaged in: 

Research on brain-inspired computing, computer vision, robot multimodal perception, and robot skill learning 

and development, etc.

Specific research work:

1. brain inspired perceptual cognitive computing, brain like pulse neural networks, etc;

2. research and application of vision detection, recognition and tracking algorithms for robots and UAVs;

3. research on robot skill learning and development based on reinforcement learning and continuous learning;

Korea University, South Korea                                                            Ph.D. 2010 – 2015.

Harbin Institute of Technology, China                                                  B.S. 2006 – 2010.                                

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences                                                                                            Associate Professor   2017- Pres.

Brain Inspired Intelligence Research Center, Institute of Automation, Chinese Academy of Sciences                                                                                                                                   Assistant Professor   2015-2017

[1] Y. F. Lu, J. W. Gao, et.al, A Cross-Scale and Illumination Invariance-Based Model for Robust Object Detection in Traffic Surveillance Scenarios, IEEE Transactions on Intelligent Transportation Systems, 2023. 24(7): 6989-6999.

[2] Y. F. Lu, X. Yang, et.al, A Novel Biologically-inspired Structural Model for Feature and Correspondence, IEEE Transactions on Cognitive and Developmental Systems, 2023. 15(2): 844-854.

[3] J.Y. Qu, Y. F. Lu*, et.al, Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection, IEEE Transactions on Neural Networks and Learning Systems, 2025. 36(3): 4934-4946.

[4] Z.Y. Gao, Y. F. Lu*, S E Li*, et.al, Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model, IEEE Transactions on Intelligent Transportation Systems, 2024. 25(10): 13067-13079.

[5] L.Y. Guo, Y. F. Lu*, et.al. Transformer-based Spiking Neural Networks for Multimodal Audio-Visual Classification, IEEE Transactions on Cognitive and Developmental Systems, 2024. 16(3): 1077-1086.

[6] Z. Fan, X. Su,Y. F. Lu*, et.al. Segment and pick any fruit: Text-prompted robotic harvesting. Pattern Recognition, 2026.179C: 113836.

[7] F．Luo, Y. F. Lu*, et.al. Temporal Dynamics Enhancer for Directly Trained Spiking Object Detectors. The 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026, Oral).

[8] Z.Y. Gao, Y. F. Lu*, et.al. DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning, IEEE International Conference on Robotics & Automation (ICRA 2026).

[9] J.Y. Qu, Y. F. Lu*, et.al, Spike-based high energy efficiency and accuracy tracker for Robot, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024). (Best Paper Award on Cognitive Robotics -Finalists)

[10] Z.Y. Li, Y. F. Lu*, et.al, Vision-Language Navigation with Continual Learning for Unseen Environments, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025).

[11] Y. F. Lu, Q. Yu, et.al, Cross Stage Partial Connections based Weighted Bi-directional Feature Pyramid and Enhanced Spatial Transformation Network for Robust Object Detection, Neurocomputing, 2022. 513: 70-82.

[12] Y. F. Lu, W. J. Zhao, What will the robots be like in the future? National Science Review, 2019. 6(5): 1059–1061.

[13] Y. Li, Y. F. Lu*, et.al, Design and Experiment of Autonomous Shield-Cutting End-Effector for Dual-Zone Maize Field Weeding. Agriculture, 2025, 15(14): 1549.

[14] D. Shang, Y. F. Lu*, et.al, Dual-Loop Online Meta-Learning withSubspace-Aware Memory Refresh for Online Class Incremental Learning, Proceedings of International Joint Conference on Neural Networks (IJCNN 2026).

[15] Y. Wang, Y. F. Lu*, LiSegAgr: Labeled Instance Segmentation for Agricultural Remote Sensing Images through Iterative SAM, International Conference on Neural Information Processing (ICONIP 2024).

[16] T. Zhang, Y. F.Lu, et.al, A Unified Multi-task Model for Leaf Disease Region Detection and Segmentation. Engineering Applications of Artificial Intelligence, 2025, 160: 111853.

[17] Y. Li, Y. F. Lu*, et.al, Intelligent Inspection System for Power Insulators based on AAV on Complex Weather Conditions. IEEE Transactions on Applied Superconductivity, 2024, 34(8): 1-4.

[18] Y. Li, Y. F. Lu*, et.al, Design of a Wireless Power Transmission System with Magnetically Integrated Compensation Network. IEEE Transactions on Applied Superconductivity, 2024, 34(8): 1-3.

[19] Y. Li, Y. F. Lu*, et.al. Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model, Energy Reports, 2022, 13(8): 807-814.

[20] C. Ma, Y. F. Lu*, Distributed Nonsynchronous Event-triggered State Estimation of Genetic Regulatory Networks with Hidden Markovian Jumping Parameters, Mathematical Biosciences and Engineering, 2022, 19(12): 13878-13910.

[21] Y. Li, Y. F. Lu*, et.al. Electromagnetic Force Analysis of a Power Transformer under the Short-Circuit Condition, IEEE Transactions on Applied Superconductivity, 2021, 31(8): 1-3.

[22] Y. F. Lu, H. Z. Zhang, et.al, Dominant Orientation Patch Matching for HMAX, Neurocomputing, 2016. 193:155-166.

[23] Y. F. Lu, T. Kang, et.al. Enhanced hierarchical model of object recognition based on a novel patch selection method in salient regions, Computer Vision, IET, 2015, 9(5): 663-672.

[24] Y. F. Lu, H. Qiao, et.al, Image Recommendation based on a Novel Biologically Inspired Hierarchical Model, Multimedia Tools and Applications, 2018, 77 (4):4323-4337.

[25] Y. F. Lu, L. H. Jia, et.al, Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment, International Journal on Wavelet, Multiresolution, and Information Processing,2019,17(2), 1940007.

[26] Y. F. Lu, M. Lim, et.al. Extended Biologically Inspired Model for Object Recognition Based on Oriented Gaussian-Hermite Moment, Neurocomputing, 2014. 139(2): 189-201.

[27] H. Z. Zhang, Y. F. Lu, et.al, B-HMAX: A fast Binary Biologically Inspired Model for Object Recognition, Neurocomputing. 2016. 218: 242-250.

[28] Y. F. Lu, H. Qiao, et.al, A Novel Biologically Inspired Hierarchical Model for Image Recommendation, 14th International Symposium on Neural Networks, Sapporo, Japan, 2017.

[29] Y. F. Lu, H. Z. Zhang, et.al. A Novel Patch Selection Method in Salient Regions of Object recognition, 30th Korean Conference of Institute of Control, Robotics and Systems, Seoul, South Korea, 2015.4.22-4.25.

[30] Y. F. Lu, A. X. Zhang, et.al. Multi-Scale Scene Text Detection Based on Convolutional Neural Network, 2019 Chinese Automation Congress (CAC). IEEE, 2019: 583-587.

[31] Y. F. Lu, H. Z. Zhang, et.al. Enhanced Hierarchical Model of Object Recognition Based on Saliency Map and Keypoint. Institute of Control, Robotics and Systems, 2015:53-54.

[32] Z.Y. Li, Y. F. Lu*, et.al. Brain-Inspired Visual Language Navigation Robot Position Deviation Correction. International Conference on Intelligent Robotics and Application, 2024: 273-287.

[33] B. C. Liu, Y. F. Lu*, et al. Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot, 2021 China Automation Congress (CAC). IEEE, 2021: 3224-3229.

[34] Y. Li, Y. F. Lu*, Dynamic Electromagnetic Force Analysis of a Power Transformer with Regulated Windings, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, 2020:1-2.

[35] F. Luo, Y. F. Lu*, et.al. HLIF: A History-Aware Model Boosting Neuronal Heterogeneity in SNNs, 2025 International Conference on Machine Intelligence and Nature-Inspired Computing (MIND). IEEE, 2025: 271-272.

[36] Z. Y. Li, Y. F. Lu*, et al. Memory mechanisms based few-shot continual learning railway obstacle detection, 2023 China Automation Congress (CAC). IEEE, 2023: 9372-9377.

[37] H. Luo, Y. F. Lu*, et al. DeepLabV3-SAM: A Novel Image Segmentation Method for Rail Transportation, 2023 3rd International Conference on Electronic Information Engineering and Computer Communication, 2023: 1-5.

[38] S. Yang, Y. F. Lu*, et al. ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving, 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025:3768–3777.

[39] C. Ma, W. Wu, Y. F. Lu. Neural Event-Triggered Optimal Filtering Co-design of Markovian Jump Systems with Hidden Mode Detections，Transactions of the Institute of Measurement and Control, 2023: 01423312221143269.

[40] J. Ren, C. Wen, L. Zhang, Y. F. Lu, et al. High Performance Point-Voxel Feature Set Abstraction With Mamba for 3D Object Detection, Expert Systems with Applications 286(5):128127.