Prof. Chen’s Personal Page
BIO
grants
teaching
BIO
Fei Chen received the B.S. degree in computer science from Xi’an Jiaotong University (XJTU), Xi’an, China, in 2006, the M.S. degree in computer science from Harbin Institute of Technology (HIT), Harbin, China, in 2008, and the Dr. Eng. degree in robotics from Fukuda Laboratory, Nagoya University, Nagoya, Japan, in 2012. Before joining CUHK, he was a researcher and founder of Active Perception and Robot Interactive Learning (APRIL) Laboratory at Italian Institute of Technology (IIT). He is currently leading Collaborative and Versatile Robots Laboratory (CLOVER) with the Department of Mechanical and Automation Engineering and CUHK T-Stone Robotics Institute. He has been acting as the PI of several EU and Italian national projects, e.g., EU FP7 AutoMAP, EU H2020 FET Chist-Era Learn-Real, VINUM and Learn-Assist as well as Hong Kong RGC ECS/GRF, ITC projects. His main research interest lies in robot mobile manipulation, robot grasping and manipulation, human-robot collaboration. He is an IEEE Senior Member. He is co-chair of IEEE Robotics and Automation Society Technical Committee on Neuro-Robotics Systems. He also serves as Associate Editor for IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, and Frontiers in Neurorobotics.
The Collaborative and Versatile Robots laboratory (CLOVER Lab) focuses on the co-evolutionary development of human-centered robotics and AI technologies for advanced robots, such as human-like mobile manipulators, humanoid robots, to perform autonomous, assistive and collaborative tasks by learning and transferring the skills from humans. We are interested with:
1. Digitalizing human skills from their demonstration during complex interactive process to support novel hybrid-model robot learning.
2. Developing autonomous and transferrable skill learning techniques from humans to virtual agents or real-world robots.
3. Exploiting robot interactive and collaborative techniques to assist human or other robots in real-world environments.
We believe that in the near future, such advanced robots can co-work harmoniously with humans in various scenarios, such as service, manufacturing, logistics, healthcare, and agri-food. We welcome collaboration with research institutes and industries to transfer the technologies. We also welcome interested and motivated talents to join the lab, and together we advance robotics for humanity.
Academic and Administrative Appointments
• Director, The Collaborative and Versatile Robots Lab (CLOVER LAB)
• Assistant Professor, The Chinese University of Hong Kong (CUHK)
• Vice-Chairman, Department of Mechanical and Automation Engineering (MAE)
Advisory Committees, Professional Organizations
• Senior Member, IEEE
• Co-Chair, IEEE RAS TC on Neuro-Robotics Systems
• Secretary, IEEE Hong Kong Society Robotics and Automation/Control Systems Joint Chapter
• Associate Editor, IEEE Transactions on Cognitive and Developmental Systems
• Associate Editor, IEEE Transactions on Emerging Topics in Computational Intelligence
• Associate Editor, Frontiers in Neurorobotics
Professional Education
• B.Eng., Computer Science, Xi’an Jiaotong University, China, 2006
• M.Eng., Computer Science, Harbin Institute of Technology, China, 2008
• Ph.D., Robotics, Nagoya University, Japan, 2012
grants
• RGC-GRF (PI: 2024-2026), Learning, Modelling and Control of Real-World Humanoid Robot-Robot Collaboration Based on Human-Human Collaboration Demonstration
• RGC-CRF (Co-PI: 2023-2026), Transferable Programming Methods for Mobile Manipulator Task Planning
• SSRFS (Co-PI: 2023-2025), Strength and Conditioning Training Robots: AI Sensing and Feedback Approach
• RGC-GRF (PI: 2023-2025), Learning and Control of Adaptive Co-Manipulation in Human-Robot Collaboration
• RGC-ECS (PI: 2022-2024), Human Tele-demonstration Based on Manipulation Skills Learning for Collaborative Dual-arm Mobile Robots
• InnoHK – Hong Kong Centre for Logistics Robotics (Co-PI: 2020-2025), Human and Robot Collaboration
• Italy-Japan LEARN-Assist (PC: 2021-2023), Assistive Robotic System for Various Dressing Tasks through Robot Learning by Demonstration via Sim-to-Real Methods
• EU H2020 Chist-Era LEARN-REAL (PI: 2019-2023), Improving Reproducibility in Learning Physical Manipulation Skills with Simulators Using Realistic Variations
• VINUM (PI: 2018-2023), Grape Vine Recognition, Manipulation and Winter Pruning Automation
• EU FP7 EUROC AutoMAP (PI: 2015-2017), Autonomous Mobile Manipulation
teaching
