出生年月

1964.8

最高学历

博士

职称/职务

研究员,中国科学院院士

工作单位

（至院、系、所）

中国科学院自动化研究所

联系方式

电话

邮箱

010-82544614

hong.qiao@ia.ac.cn

招生方向

（可选填，最多3个）

招生方向1

计算机方向

招生方向2

控制方向

招生方向3

教育背景

1982-1986，西安交通大学机械系，本科

1986-1989，西安交通大学机械系，硕士

1990-1992，英国思克莱德大学，硕士

1992-1995，英国德蒙福特大学，博士

研究方向

机器人“手”-“眼”-“脑”融合智能研究与应用，包括：

（1）工业机器人操作与控制（手）；

（2）机器人与人工智能（眼）；

（3）生物启发式与类脑智能机器人（脑）等。

任职经历

1995-1997，英国德蒙福特大学，研究员

1997-2000，香港城市大学，研究助教授

2000-2002，香港城市大学，助理教授

2002-2004，英国曼彻斯特大学, 讲师，博导（永久职位）

2004.8至今，中国科学院 “百人计划”研究员

2018年至今，复杂系统管理与控制国家重点实验室 副主任

主持、参与

项目

1.  项目参与人，国家基金委重大研究计划（共融机器人基础理论与关键技术研究）集成项目“基于群体智能机器人操作系统的集成和创新”，2020.1-2024.12

2. 项目负责人，中科院战略先导，类脑器件与系统，2018

3. 项目负责人、课题负责人， 国家科技部国家重点研发计划智能机器人专项“机器人智能发育理论、方法与验证”，2017

4. 项目负责人，国家自然科学基金委国家重大科研仪器研制项目（自由申请） “面向‘神经机制-类脑计算模型’研究的综合智能实验系统研制”，2016

5. 项目负责人，国家自然科学基金委重大研究计划（共融机器人基础理论与关键技术研究） “无人机与有人机共融博弈的基础理论与关键技术研究”，2016

荣誉、奖项

1. 荣誉称号

2021 中国科学院院士

2007 国家杰出青年基金获得者

2009 “新世纪百千万人才工程”国家级人选

2018 IEEE Fellow

2. 所获奖励

2014 获得国家自然科学二等奖（机器人领域获得的三个国家自然科学奖之一）

2012 获得北京市科学技术奖一等奖（基础研究类，排名第一）

2018 获中国自动化学会技术科学一等奖（排名第一）

2015 获得北京市科学技术奖二等奖（技术发明类）

科研成果

（论文、著作、

专利等）

[1] H. Qiao, J. Chen, X. Huang. A survey of brain-inspired intelligent robots with integration of vision, decision, motion control and musculoskeletal systems[J]. IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2021.3071312

[2] Ziyu Chen, Yang Liu, Wei He, Hong Qiao, Haibo Ji. Adaptive neural networkbased trajectory tracking control for a nonholonomic wheeled mobile robot with velocity constraints [J], IEEE Transactions on Industrial Electronics, 2021, 68(6): 5057-5067

[3] E. Kang, H. Qiao, J. Gao et al., Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints [J]. ISA Transactions, 2021 (109):89-101

[4] J Gao, E. Kang, W He, et al. Adaptive model-based dynamic event-triggered output feedback control of a robotic manipulator with disturbance [J]. ISA transactions, 2021.

[5] X. Yang, Z. Y. Liu, H. Qiao, “Incorporating Discrete Constraints Into Random Walk-Based Graph Matching,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 4, pp. 1406-1416, 2020.

[6] S. L. Zhong, J. H. Chen, X. Y. Niu, H. Fu, H. Qiao, “Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection,” IEEE Transactions on Cognitive and Developmental Systems, vol. 12, no. 3, pp. 601-617, 2020.

[7] X. Yang, Z. Y. Liu, H. Qiao, “A Continuation Method for Graph Matching Based Feature Correspondence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 8, pp. 1809-1822, 2020.

[8] F. Y. Zhang, Z. Y. Liu, F. Z. Xiong, J. H. Su, H. Qiao, “WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning,” Robotics and Autonomous Systems, vol. 130, pp. 1-9, 2020.

[9] S. L. Xu, L. Feng, S. L. Liu, J. Zhou, H. Qiao, “Multi-feature weighting neighborhood density clustering,” Neural Computing & Applications, vol. 32, no. 13, pp. 9545-9565, 2020.

[10]  X. Y. Huang, X. L. Nie, H. Qiao, “PolSAR Image Feature Extraction via Co-Regularized Graph Embedding,” Remote Sensing, vol. 12, no. 11, pp. 1-19, 2020.

[11]  Y. D. Ji, Y. L. Li, W. Wu, H. Fu, H. Qiao, “Mode-dependent event-triggered tracking control for uncertain semi-Markov systems with application to vertical take-off and landing helicopter,” Measurement & Control, vol. 53, no. 5-6, pp. 954-961, 2020.

[12] X. Q. Li, Y. Qian, R. Li, X. Y. Niu, H. Qiao, “Robust form-closure grasp planning for 4-pin gripper using learning-based Attractive Region in Environment,” Neurocomputing, vol. 384, pp. 268-281, 2020.

[13] X. Yang, Z. Y. Liu, H. Qiao, J. H. Su, D. X. Ji, A. Y. Zang, H. Huang, “Graph-Based Registration and Blending for Undersea Image Stitching,” Robotica, vol. 38, no. 3, pp. 396-409, 2020.

[14] S. L. Xu, L. Feng, S. L. Liu, H. Qiao, “Self-adaption neighborhood density clustering method for mixed data stream with concept drift,” Engineering Applications of Artificial Intelligence, vol. 89, pp. 1-14, 2020.

[15] C. F. Liu, L. Feng, S. Guo, H. B. Wang, S. L. Liu, H. Qiao, “An incrementally cascaded broad learning framework to facial landmark tracking,” Neurocomputing, vol. 410, pp. 125-137, 2020.

[16] Y. Yu, S. Luo, S. L. Liu, H. Qiao, Y. Liu, L. Feng, “Deep attention based music genre classification,” Neurocomputing, vol. 372, pp. 84-91, 2020.

[17] S. L. Liu, X. Liu, G. Huang, H. Qiao, L. Y. Hu, D. Jiang, A. B. Zhang, Y. Liu, G. Guo, “FSD-10: A fine-grained classification dataset for figure skating,” Neurocomputing, vol. 413, pp. 360-367, 2020.

[18] S. L. Liu, S. Guo, W. Wang, H. Qiao, Y. Wang, W. B. Luo, “Multi-view laplacian eigenmaps based on bag-of-neighbors for RGB-D human emotion recognition,” Information Sciences, vol. 509, pp. 243-256, 2020.

[19] F. Z. Xiong, Z. Y. Liu, K. Z. Huang, X. Yang, H. Qiao, A. Hussain, “Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning,” Neural Networks, vol. 129, pp. 163-173, 2020.

[20] F. Z. Xiong, Z. Y. Liu, K. Z. Huang, X. Yang, H. Qiao, “State Primitive Learning to Overcome Catastrophic Forgetting in Robotics,” Cognitive Computation, pp. 1-9, 2020.

[21] S. Guo, L. Feng, Z. B. Feng, Y. H. Li, Y. Wang, S. L. Liu, H. Qiao, “Multi-view laplacian least squares for human emotion recognition,” Neurocomputing, vol. 370, pp. 78-87, 2019.

[22] R. Li, H. Qiao, “A Survey of Methods and Strategies for High-Precision Robotic Grasping and Assembly Tasks - Some New Trends,” IEEE-ASME Transactions on Mechatronices, vol. 24, no. 6, pp. 2718- 2732, 2019.

[23] W. Zhang, X. Y. He, W. Z. Lu, H. Qiao, Y. B. Li, “Feature Aggregation With Reinforcement Learning for Video-Based Person Re-Identification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3847-3852, 2019.

[24] J. H. Su, B. Chen, C. K. Liu, X. Yang, Z. Y. Liu, H. Qiao, “Integrated thermal assembly using hierarchical kernel regression method,” Advanced Robotics, vol. 33, no. 22, pp. 1194-1208, 2019.

[25] E. H. Zheng, Q. N. Wang, H. Qiao, “Locomotion Mode Recognition With Robotic Transtibial Prosthesis in Inter-Session and Inter-Day Applications,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 9, pp. 1836-1845, 2019.

[26] J. J. Zhou, J. H. Chen, H. Deng, H. Qiao, “From Rough to Precise: Human-Inspired Phased Target Learning Framework for Redundant Musculoskeletal Systems,” Frontiers in Neurorobotics, vol. 13, pp. 1-14, 2019.

[27] J. H. Su, B. Chen, H. Qiao, Z. Y. Liu, “Caging a novel object using multi-task learning method,” Neurocomputing, vol. 351, pp. 146-155, 2019.

[28] J. H. Chen, S. L. Zhong, E. L. Kang, H. Qiao, “Realizing human-like manipulation with a musculoskeletal system and biologically inspired control scheme,” Neurocomputing, vol. 339, pp. 116-129, 2019.

[29] X. Y. Xi, Y. K. Lou, P. Wang, H. Qiao, “Salient object detection based on an efficient End-to-End Saliency Regression Network,” Neurocomputing, vol. 323, pp. 265-276, 2019.

[30] F. Z. Xiong, B. Sun, X. Yang, H. Qiao, K. Z. Huang, A. Hussain and Z. Y. Liu, “Guided Policy Search for Sequential Multitask Learning,” IEEE Transaction on Systems, Man, and Cybernetics: Systems, vol. 49, no. 1, pp. 216-226, 2019.

[31] L. Feng, S. L. Xu, F. Wang, S. L. Liu, H. Qiao, “Rough extreme learning machine: A new classification method based on uncertainty measure,” Neurocomputing, vol. 325, pp. 269-282, 2019.

[32] X. L. Nie, S. G. Ding, X. Y. Huang, H. Qiao, B. Zhang, Z. P. Jiang, “An Online Multiview Learning Algorithm for PolSAR Data Real-Time Classification,” IEEE Journal of Selected Topics in Applied Earth Observations And Remote Sensing, vol. 12, no. 1, pp. 302-320, 2019.

[33] Y. L. Li, L. H. Jia, Z. D. Wang, Y. Qian, H. Qiao, “Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning,” Neurocomputing, vol. 334, pp. 11-24, 2019.

[34] L. Zhang, Z. Y. Liu, S. F. Zhang, X. Yang, H. Qiao, K. Z. Huang, A. Hussain, “Cross-modality interactive attention network for multispectral pedestrian detection,” Information Fusion, vol. 50, pp. 20-29, 2019.

[35] Y. F. Lu, L. H. Jia, H. Qiao, Y. Li, Z. S. Qi, “Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment,” International Journal of Wavelets, Multiresolution and Information Processing, vol. 17, no. 2, pp. 1-16, 2019.

[36] C. Ma, R. Li, H. Qiao, “Non-fragile consensus control of networked robotic manipulators with topology-dependent memory,” Assembly Automation, vol. 38, no. 5, pp. 625-634, 2018.

[37] X. Huang, W. Wei, H. Qiao, Y. D. Ji, “Brain-Inspired Motion Learning in Recurrent Neural Network with Emotion Modulation,” IEEE Transaction on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1153-1164, 2018.

[38] S. L. Liu, J. Wu, L. Feng, H. Qiao, Y. Liu, W. B. Luo, W. Wang, “Perceptual uniform descriptor and ranking on manifold for image retrieval,” Information Sciences, vol. 424, pp. 235-249, 2018.

[39] Y. F. Lu, H. Qiao, Y. Li, L. H. Jia, “Image recommendation based on a novel biologically inspired hierarchical model,” Multimedia Tools and Applications, vol. 77, no. 4, pp. 4323-4337, 2018.

[40] C. Ma, H. Qiao, E. L. Kang, “Mixed H-infinity and Passive Depth Control for Autonomous Underwater Vehicles with Fuzzy,” International Journal of Fuzzy Systems, vol. 20, no. 2, pp. 621-629, 2018.

[41] F. F. Li, H. Qiao, B. Zhang, “Discriminatively boosted image clustering with fully convolutional auto-encoders,” Pattern Recognition, vol. 83, pp. 161-173, 2018.

[42] S. G. Ding, X. Y. Xi, Z. Y. Liu, H. Qiao, B. Zhang, “A Novel Manifold Regularized Online Semi-supervised Learning Model,” Cognitive Computation, vol. 10, no. 1, pp. 49-61, 2018.

[43] P. Yin, H. Qiao, W. Wu, L. Qi, Y. L. Li, S. L. Zhong, B. Zhang, “A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity,” IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 420-431, 2018.

[44] X. Y. Huang, H. Qiao, B. Zhang, X. L. Nie, “Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding,” IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2966-2979, 2018.

[45] S. G. Ding, X. L. Nie, H. Qiao, B. Zhang, “A Fast Algorithm of Convex Hull Vertices Selection for Online Classification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 792-806, 2018.

[46] S. G. Liu, L. Feng, Y. Liu, H. Qiao, J. Wu, W. Wang, “Manifold Warp Segmentation of Human Action,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1414-1426, 2018.

[47] S. H. Ying, X. J. Wen, J. Shi, Y. X. Peng, J. G. Peng, H. Qiao, “Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 2731-2742, 2018.

[48] X. Yang, H. Qiao, Z. Y. Liu, “An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 3295-3300, 2018.

[49] X. Y. Huang, X. L. Nie, W. Wu, H. Qiao, B. Zhang, “ SAR target configuration recognition based on the biologically inspired model,” Neurocomputing, vol. 234, pp. 185-191, 2017.

[50] R. Li, H. Qiao, “Condition and Strategy Analysis for Assembly based on Attractive Region in Environment,” IEEE/ASME Transactions on Mechatronics, vol. 22, no. 5, pp. 2218-2228, 2017.

[51] C. Ma, H. Qiao, “Distributed asynchronous event-triggered consensus of nonlinear multi-agent systems with disturbances: An extended dissipative approach,” Neurocomputing, vol. 243, pp. 103-114, 2017.

[52] X. Yang, Z. Y. Liu, H. Qiao, J. H. Su, “Probabilistic hypergraph matching based on affinity tensor updating,” Neurocomputing, vol. 269, pp. 142-147, 2017.

[53] J. H. Su, R. Li, H. Qiao, J. Xu, Q. L. Ai, J. K. Zhu, “Study on dual peg-in-hole insertion using of constraints formed in the environment,” Industrial Robot: The International Journal of Robotics Research and Application, vol. 44, no. 6, pp. 730-740, 2017.

[54] X. Y. Huang, B. Zhang, H. Qiao, X. L. Nie, “Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 11, pp. 2102-2106, 2017.

[55] X. Yang, H. Qiao, Z. Y. Liu, “Point correspondence by a new third order graph matching algorithm,” Pattern Recognition, vol. 65, pp. 108-118, 2017.

[56] X. Yang, Z. Y. Liu, H. Qiao, Y. B. Song, S. N. Ren, D. X. Ji, S. W. Zheng, “Underwater image matching by incorporating structural constraints,” International Journal of Advanced Robotic Systems, vol. 14, no. 6, pp. 1-10,  2017.

[57] J. H. Su, H. Qiao, C. K. Liu, Y. B. Song, A. L. Yang, “Grasping Objects: The Relationship Between the Cage and the Form-Closure Grasp,” IEEE Robotics & Automation Magazine, vol. 24, no. 3, pp. 84-96, 2017.

[58] B. Shen, Z. D. Wang, H. Qiao, “Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 5, pp. 1152-1163, 2017.

[59] J. Sun, P. Wang, Z. K. Qin, H. Qiao, “Effective self-calibration for camera parameters and hand-eye geometry based on two feature points motions,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 2, pp. 370-380, 2017.