Dr Kunpeng Yao
- Position: Lecturer (Assistant Professor) in Robotics
- Areas of expertise: robotic grasping, dexterous manipulation, task and motion planning, robot skill transfer, machine learning in robotics, human motor control, motor skill acquisition
- Email: K.Yao@leeds.ac.uk
- Location: 2.221 Sir William Henry Bragg Building
- Website: Googlescholar
Profile
I am currently a Lecturer (Assistant Professor) in Robotics at the School of Computer Science.
I received my Ph.D. degree in Robotics, Control, and Intelligent Systems from the Swiss Federal Institute of Technology (EPFL), Switzerland, in 2022. I was a Postdoctoral Fellow at the Newman Laboratory, Massachusetts Institute of Technology (MIT), U.S. (2024-2025), and a postdoctoral researcher at the Learning Algorithms and Systems Laboratory (LASA), EPFL, Switzerland (2022-2023). I received my B.Sc. degree from Shanghai Jiao Tong University (SJTU), China, and my M.Sc. degree from the Technical University of Munich (TUM), Munich, Germany.
I am the recipient of an SNSF Postdoc.Mobility Fellowship (Swiss National Science Foundation), DAAD AInet Fellow, and EPFL EDRS PhD Distinction Nomination.
I am a member of IEEE (2022-), IEEE Robotics and Automation Society (2023-), and Society for the Neural Control of Movement (2022).
Research interests
My primary research interests include robotic dexterous grasping and manipulation, task and motion planning, tactile sensing, and human motor control. I am particularly interested in enhancing the dexterity of robotic systems, which is a critical research frontier significant to the broad adoption of robots in our society.
Achieving generalizable manipulation skills is crucial for reducing robot deployment costs and enabling automation across diverse industries, from flexible manufacturing to healthcare and domestic assistance. Inspired by the human ability to skillfully manipulate objects through coordinated sensorimotor control, I aim to develop generalizable and scalable manipulation skills for robots through platform-agnostic algorithms that afford dexterity across tasks, environments, and systems. Specifically, I am interested in (1) modeling generalized multi-modal skill primitives, (2) context-aware adaptive task and motion planning, (3) generalizing robot skills through transfer learning, and (4) designing versatile robotic manipulators.
I welcome inquiries from highly motivated and qualified applicants worldwide who are interested in PhD study.
Qualifications
- Ph.D. in Robotics, Control, and Intelligent Systems
- M.Sc. in Electrical Engineering and Information Technology
- B.Sc. in Electronic Information and Electrical Engineering
Professional memberships
- Member, IEEE
- Member, IEEE Robotics and Automation Society (RAS)