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崔允端

副研究员

中国科学院深圳先进技术研究院

关于我

课题组诚挚欢迎本科同学报考推免研究生,在读研究生同学客座访问,博士毕业生申请博士后,请将简历发送至 yd.cui[at]siat.ac.cn

课题组成员
研三:尚致违,李任行(客座学生,中科大软件学院)
研二:夏镭,龚茗荣,王金成(南科大联培)
研一:黄文俊,缪晨阳,李尚德(南科大联培)

课题组承担项目
国家自然科学基金青年项目,“面向环境不确定性的强化学习无人船控制方法”,30万,2022-2024,主持,在研
中国科学院率先行动人才择优项目,500万,2021-2023,主持,在研
国家重点研发计划,“物联网与智慧城市关键技术及示范专项”重点专项子课题,854万,2020-2023,参与,在研

工作经历


2020年 四月至今
副研究员
中国科学院深圳先进技术研究院

2017年 十月 - 2020年 三月
博士后研究员
日本奈良先端科学技术大学院大学

教育经历


2014年 十月 - 2017年 九月
博士
日本奈良先端科学技术大学院大学

2012年 九月 -2014年 九月
硕士
日本同志社大学

2008年 八月 - 2012年 七月
学士
西安电子科技大学

奖励


2021年 五月
深圳市海外高层次人才(孔雀计划)B类认定

2020年 十一月
中国科学院率先行动人才择优计划B类

2019年 十一月
日本计测自动控制学会青年作者奖 (IROS 2019)
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems

2018年 十月
2018年日本神经网络学会优秀论文奖

2018年 三月
最优秀学生奖 (博士课程)
日本奈良先端科学技术大学院大学

2016年 十一月
最优秀论文奖
2016 IEEE-RAS 16th International Conference on Humanoid Robots

2016年 四月 - 2017年 九月
日本文部省奖学金 (MEXT)

学术活动


会员

The Institute of Electrical and Electronic Engineers (IEEE)

审稿人

ICRA, IROS, CoRL, Humanoids

IEEE Transactions on Industrial Informatics

IEEE Robotics and Automation Letters

IEEE Transactions on Cybernetics

Automatica

Neural Networks

Neurocomputing

Autonomous Robots

Robotics and Autonomous Systems

Ocean Engineering

IEEE Access

Advanced Robotics

编辑

Special issue “Deformable object manipulation”, Frontiers in Neurorobotics

编委会

IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) 2019

发表论文

期刊


  1. Yunduan Cui, Lei Peng, and Huiyun Li. “Filtered Probabilistic Model Predictive Control-based Reinforcement Learning for Unmanned Surface Vehicles.” IEEE Transactions on Industrial Informatics, 2022. link

  2. Dezhou Xu, Yunduan Cui, Jiaye Ye, Suk Won Cha, Aimin Li, and Chunhua Zheng. “A soft actor-critic-based energy management strategy for electric vehicles with hybrid energy storage systems.” Journal of Power Sources, 2022. link

  3. Yujun Lai, Gavin Paul, Yunduan Cui, and Takamitsu Matsubara. “User intent estimation during robot learning using physical human robot interaction primitives.” Autonomous Robots, 2022. link

  4. Wei Li, Jiaye Ye, Yunduan Cui, Namwook Kim, Suk Won Cha, and Chunhua Zheng. “A Speedy Reinforcement Learning-Based Energy Management Strategy for Fuel Cell Hybrid Vehicles Considering Fuel Cell System Lifetime.” International Journal of Precision Engineering and Manufacturing-Green Technology, 2021. link

  5. Cheng-Yu Kuo, Andreas Schaarschmidt, Yunduan Cui, Tamim Asfour, and Takamitsu Matsubara. “Uncertainty-aware Contact-safe Model-based Reinforcement Learning.” IEEE Robotics and Automation Letters (with ICRA 2021), 2021. link

  6. Yunduan Cui, Osaki Shigeki, and Takamitsu Matsubara. “Autonomous Boat Driving System using Sample-efficient Model Predictive Control-based Reinforcement Learning Approach.” Journal of Field Robotics, 2020. link

  7. Yunduan Cui, Junichiro Ooga, Akihito Ogawa, and Takamitsu Matsubara. “Probabilistic Active Filtering with Gaussian Processes for Occluded Object Search in Clutter.” Applied Intelligence, 2020. link

  8. Lingwei Zhu, Yunduan Cui, Go Takami, Hiroaki Kanokogi, and Takamitsu Matsubara. “Scalable Reinforcement Learning for Plant-wide Control of Vinyl Acetate Monomer Process.” Control Engineering Practice, 2020. link

  9. Yoshihisa Tsurumine, Yunduan Cui, Eiji Uchibe, and Takamitsu Matsubara. “Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation.” Robotics and Autonomous Systems, 2018. link

  10. Yunduan Cui, James Poon, Jaime Valls Miro, Kimitoshi Yamazaki, Kenji Sugimoto, and Takamitsu Matsubara. “Environment-adaptive interaction primitives through visual context for human–robot motor skill learning.” Autonomous Robots, 2018. link

  11. Yunduan Cui, Takamitsu Matsubara, and Kenji Sugimoto. “Kernel dynamic policy programming: Applicable reinforcement learning to robot systems with high dimensional states.” Neural Networks, 2017. (2018 Japanese Neural Network Society Best Paper Award) link

  12. Yunduan Cui, Takamitsu Matsubara, and Kenji Sugimoto. “Pneumatic artificial muscle-driven robot control using local update reinforcement learning.” Advanced Robotics, 2017. link

国际会议


  1. Zhiwei Shang, Huiyun Li, and Yunduan Cui. “Shiftable Dynamic Policy Programming for Efficient and Robust Reinforcement Learning Control.” IEEE International Conference on Robotics and Biomimetics (ROBIO) 2021.

  2. Naitian Deng, Yunduan Cui, Shitian Zhang, and Huiyun Li. “Autonomous Vehicle Motion Planning using Kernelized Movement Primitives.” International Symposium on Networks, Computers and Communications (ISNCC) 2021. link

  3. Shitian Zhang, Yunduan Cui, Naitian Deng, and Huiyun Li. “Model Predictive Control of Autonomous Driving using Unscented Kalman Filter with Sparse Spectrum Gaussian Processes.” International Symposium on Networks, Computers and Communications (ISNCC) 2021. link

  4. Lingwei Zhu, Yunduan Cui, and Takamitsu Matsubara. “Dynamic Actor-Advisor Programming for Scalable Safe Reinforcement Learning.” IEEE International Conference on Robotics and Automation (ICRA) 2020. link

  5. Cheng-Yu Kuo, Yunduan Cui, and Takamitsu Matsubara. “Sample-and-computational-efficient Probabilistic Model Predictive Control with Random Features.” IEEE International Conference on Robotics and Automation (ICRA) 2020. link

  6. Yunduan Cui, Shigeki Osaki, Takamitsu Matsubara. “Reinforcement Learning Ship Autopilot: Sample-efficient and Model Predictive Control-based Approach.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019. ArXiv preprint arXiv:1901.07905 SICE International Young Authors Award 2019 (IROS) link

  7. Yoshihisa Tsurumine, Yunduan Cui, Kimitoshi Yamazaki, Takamitsu Matsubara. “Generative Adversarial Imitation Learning with Deep P-Network for Robotic Cloth Manipulation” IEEE-RAS International Conference on Humanoid Robots (Humanoids) 2019. link

  8. James Poon, Yunduan Cui, Junichiro Ohga, Akihito Ogawa, and Takamitsu Matsubara. “Probabilistic Active Filtering for Object Search in Clutter.” IEEE International Conference on Robotics and Automation (ICRA) 2019. link

  9. James Poon, Yunduan Cui, Jaime Valls Miro, Takamitsu Matsubara. “Learning Mobility Aid Assistance via Decoupled Observation Models.” International Conference on Control, Automation, Robotics and Vision (ICARCV 2018). link

  10. Yunduan Cui, Lingwei Zhu, Morihiro Fujisaki, Hiroaki Kanokogi, and Takamitsu Matsubara. “Factorial Kernel Dynamic Policy Programming for Vinyl Acetate Monomer Plant Model Control.” IEEE International Conference on Automation Science and Engineering (CASE) 2018. link

  11. Takamitsu Matsubara, Yu Norinaga, Yuto Ozawa, and Yunduan Cui. “Policy Transfer from Simulations to Real World by Transfer Component Analysis.” IEEE International Conference on Automation Science and Engineering (CASE) 2018. link

  12. Yoshihisa Tsurumine, Yunduan Cui, Eiji Uchibe, and Takamitsu Matsubara. “Deep Dynamic Policy Programming for Robot Control with Raw Images.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017. link

  13. James Poon, Yunduan Cui, Jaime Valls Miro, Takamitsu Matsubara, and Kenji Sugimoto. “Local Driving Assistance from Demonstration for Mobility Aids.” IEEE International Conference on Robotics and Automation (ICRA) 2017. link

  14. Yunduan Cui, James Poon, Takamitsu Matsubara, Jaime Valls Miro, Kenji Sugimoto, and Kimitoshi Yamazaki. “Environment-adaptive Interaction Primitives for Human-Robot Motor Skill Learning.” IEEE-RAS International Conference on Humanoid Robots (Humanoids) 2016. link

  15. Yunduan Cui, Takamitsu Matsubara, and Kenji Sugimoto. “Kernel Dynamic Policy Programming: Practical Reinforcement Learning for High-dimensional Robots.” IEEE-RAS International Conference on Humanoid Robots (Humanoids) 2016. ( Best Oral Paper Award) link

  16. Yunduan Cui, Takamitsu Matsubara, and Kenji Sugimoto. “Local Update Dynamic Policy Programming in reinforcement learning of pneumatic artificial muscle-driven humanoid hand control.” IEEE-RAS International Conference on Humanoid Robots (Humanoids) 2015. link

  17. Yunduan Cui, Kazuhiko Takahashi, and Masafumi Hashimoto. “Remarks on quaternion neural network based controller with application to an inverted pendulum.” 2014 SICE Annual Conference. link

  18. Kazuhiko Takahashi, Sae Takahashi, Yunduan Cui and Masafumi Hashimoto. “Remarks on computational facial expression recognition from HOG features using quaternion multi-layer neural network.” 2014 International Conference on Engineering Applications of Neural Networks. link

  19. Yunduan Cui, Kazuhiko Takahashi, and Masafumi Hashimoto. “Remarks on robot controller application of Clifford multi-layer neural networks.” 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC). link

  20. Yunduan Cui, Kazuhiko Takahashi, and Masafumi Hashimoto. “Design of control systems using quaternion neural network and its application to inverse kinematics of robot manipulator.” 2013 IEEE/SICE International Symposium on System Integration (SII). link

联系方式

邮箱

yd.cui[at]siat.ac.cn

cuiyunduan[at]hotmail.com