Plenary speakers

Prof. Jianwei Zhang, University of Hamburg, Germany

Talk Title: Robust robot perception and manipulation systems synergically driven by world models and crossmodal learning (May 20)

Jianwei Zhang is professor and Director of Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Germany. He is Member of the German National Academy of Engineering Sciences and International Member of Chinese Academy of Engineering. He is Distinguished Visiting Professor of Tsinghua University as well. He received both his Bachelor of Engineering (1986, Computer Control, with distinction) and Master of Engineering (1989, AI) at the Department of Computer Science of Tsinghua University, Beijing, China, and his PhD (1994, Robotics) at the Institute of Real-Time Computer Systems and Robotics, Department of Computer Science, University of Karlsruhe, Germany. Jianwei Zhang´s research interests include 3D robot perception, multimodal information (visual, auditory, tactile, etc.) processing; cognitive sensor fusion for robot perception; real-time learning and modelling of sensory-motor control tasks; learning and control of robot grasping and in-hand manipulation; experience-based robot learning; bi-manual robot assembly of 3D aggregates; mobile manipulation service robots; natural human-robot interaction, etc. In these areas, he has published over 500 journal and conference papers, and spinned off several startup companies in areas of high-resolution 3D point cloud cameras, force-controlled robot arms and robot-based medical devices, etc. He is the General Chair of IEEE MFI (Multisensor Fusion and Integration) 2012, IEEE/RSJ IROS (Intelligent Robots and Systems) 2015, and HCR (Human-Centred Robotics) 2018, and Associated VP of IEEE Robotics Automation Society CAB, etc. He is the coordinator of the DFG/NSFC Transregional Collaborative Research Centre SFB/TRR169 “Crossmodal Learning: Adaptivity, Prediction and Interaction” since 2015. He also leads several EU robotics projects, including the RACE (Robustness by Autonomous Competence Enhancement) project that was the first to apply high-level learning, planning and reasoning AI methods to service robots. He has received multiple best paper awards at several major robotic conferences.




Prof. Toshio Fukuda, Nagoya University, Japan

Talk Title: The AI robot for 2050 (May 21)

Toshio Fukuda graduated from Waseda University, Tokyo, Japan in 1971 and received the Master of Engineering degree and the Doctor of Engineering degree both from the University of Tokyo, in 1973 and 1977, respectively. He joined the National Mechanical Engineering Laboratory in Japan in 1977, the Science University of Tokyo in 1981, and then joined Department of Mechanical Engineering, Nagoya University, Japan in 1989. At present, he is Professor of Dept. of Micro and Nano System Engineering and Dept. of Mechano- Informatics and Systems, Nagoya University, Japan. He is director of Center for Micro and Nano Mechatronics. He is mainly engaging in the research fields of intelligent robotic system, micro and nano robotics, bio-robotic system, and technical diagnosis and error recovery system. He was the President of IEEE Robotics and Automation Society (1998-1999), Director of the IEEE Division X, Systems and Control (2001-2002), the Founding President of IEEE Nanotechnology Council (2002-2005), and Region 10 Director-elect (2011-2012). He was Editor-in-Chief of IEEE/ASME Trans. Mechatronics (2000-2002). He was the Founding General Chairman of IEEE International Conference on Intelligent Robots and Systems (IROS) held in Tokyo (1988). He was Founding Chair of the IEEE Workshop on Advanced Robotics Technology and Social Impacts (ARSO, 2005), Founding Chair of the IEEE Workshop on System Integration Internatioal (SII, 2008), Founding Chair of the International Symposium on Micro- Nano Mechatronics and Human Science (MHS, 1990-2011). He has received many awards such as IEEE Eugene Mittelmann Achievement Award (1997), IEEE Third Millennium Medal (2000) , IEEE Robotics and Automation Pioneer Award (2004), IEEE Transaction Automation Science and Engineering Googol Best New Application Paper Award (2007), George Saridis Leadership Award in Robotics and Automation (2009), IEEE Robotics and Automation Technical Field Award (2010). IEEE Fellow (1995). SICE Fellow (1995). JSME Fellow (2002), RSJ Fellow (2004), VRSJ Fellow (2011) and member of Science Council of Japan (2008- ).




Prof. Masayoshi Tomizuka, University of California, Berkeley

Talk Title: Human-Robot Collaboration and Interaction (May 22)

Masayoshi Tomizuka received his Ph. D. degree in Mechanical Engineering from the Massachusetts Institute of Technology in February 1974. In 1974, he joined the faculty of the Department of Mechanical Engineering at the University of California at Berkeley, where he currently holds the Cheryl and John Neerhout, Jr., Distinguished Professorship Chair and serves as Associate Dean for the Faculty in the College of Engineering. His current research interests are control theory, merging model based control and machine learning, mechatronic systems such as intelligent robots and autonomous vehicles. He served as Program Director of the Dynamic Systems and Control Program of the National Science Foundation (2002-2004). He has supervised about 140 Ph. D. students to completion. He is the recipient of the Charles Russ Richards Memorial Award (ASME, 1997), the Rufus Oldenburger Medal (ASME, 2002), the John R. Ragazzini Award (AACC, 2006), the Richard Bellman Control Heritage Award (AACC, 2018) and the Nichols Medal (IFAC, 2020). He is an honorary member of ASME, Life Fellow of IEEE and is a member of the United State National Academy of Engineering.

Collaboration and/or interaction of human-robot takes place in a variety of shared spaces such as factory floor, warehouse, office and road. My research group studied this problem to establish a set of design principles of safe and efficient robot collaboration systems (SERoCS) for the next generation co-robots, which consists of robust cognition algorithms for environment monitoring, optimal task planning algorithms for safe human-robot collaboration, and safe motion planning and control algorithms for safe human-robot interactions (HRI). Autonomous vehicles share roads with human driven vehicles, and their driving policy must pay attention to human drivers. In particular, we studied how we may reflect the courtesy in the driving policy of automated vehicles to other surrounding vehicles. More recently, we have looked into Interactive behavior modeling of multiple agents in scenarios where agents need to avoid collisions and cooperate at the same time. Humans can interact with others without explicit communication and navigate in scenarios when cooperation is required. We introduced a model to describe such situations where each agent solves an Imagined Potential Game (IPG) based on its estimation on other without communication.