IRCE 2022 Keynote Speakers

Prof. Fuchun Sun
Tsinghua University, China
(IEEE Fellow)

Biodata: Dr. Fuchun Sun is professor of Department of Computer Science and Technology and President of Academic Committee of the Department, Tsinghua University, deputy director of State Key Lab. of Intelligent Technology & Systems, Beijing, China. He also serve as Vice president of China Artificial Intelligence Society and executive director of China Automation Society. His research interests include robotic perception and intelligent control. He has won the Champion of Autonoumous Grasp Challenges in IROS2016 and IROS 2019.
Dr. Sun is the recipient of the excellent Doctoral Dissertation Prize of China in 2000 by MOE of China and the Choon-Gang Academic Award by Korea in 2003, and was recognized as a Distinguished Young Scholar in 2006 by the Natural Science Foundation of China. He served as an associated editor of IEEE Trans. on Neural Networks during 2006-2010, IEEE Trans. On Fuzzy Systems during 2011-2018, IEEE Trans. on Cognitive and Developement since 2018 and IEEE Trans. on Systems, Man and Cybernetics: Systems since 2015.

Speech Title: Skill Learning in Dynamic Scene for Robot Operations

Abstract: The robot AI is dominated by physical interaction in closed-loop form. It not only emphasizes the perception and processing of simulated human brain information, but also emphasizes brain body cooperation to solve the dynamic, interactive and adaptive problems of behavior learning in dynamic scene. As the core of robot AI, skill learning for robot operations is a difficult and hot issue in current research. In view of the problems that the existing skill learning methods do not make use of the teaching samples efficiently and cannot achieve efficient strategy learning, and the imitation learning algorithm is sensitive to the teaching preference characteristics and the local operation space. This talk studies the skill learning for robot operations in the complex dynamic environment, and proposes skill learning framework based on human preference. By using the guidance of the existing poor teaching samples, this talk proposes an optimization method of reinforcement learning based on teaching imitation, which improves the sample utilization rate and strategy learning performance of skill learning in high-dimensional space. Finally, the future development of robot skill learning is prospected.

 

Prof. Maria Pia Fanti
Polytechnic University of Bari, Italy
(IEEE Fellow)

Biodata: Maria Pia Fanti (fellow of IEEE) received the Laurea degree in electronic engineering from the University of Pisa, Pisa, Italy, in 1983. She was a visiting researcher at the Rensselaer Polytechnic Institute of Troy, New York, in 1999. Since 1983, she has been with the Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy, where she is currently a Full Professor of system and control engineering and Chair of the Laboratory of Automation and Control.
Her research interests include modeling and control of complex systems, intelligent transportation systems, smart logistics; Petri nets; consensus protocols; fault detection.
Prof. Fanti has published more than +300 papers and two textbooks on her research topics. She was senior editor of the IEEE Trans. on Automation Science and Engineering and member at large of the Board of Governors of the IEEE Systems, Man, and Cybernetics Society. Currently, she is Associate Editor of the IEEE Trans. on Systems, Man, and Cybernetics: Systems, member of the AdCom of the IEEE Robotics and Automaton Society, and chair of the Technical Committee on Automation in Logistics of the IEEE Robotics and Automation Society. Prof. Fanti was General Chair of the 2011 IEEE Conference on Automation Science and Engineering, the 2017 IEEE International Conference on Service Operations and Logistics, and Informatics and the 2019 Systems, Man, and Cybernetics Conference.