Keynote Speakers

Jianwei Zhang 张建伟
Institute of Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Germany
德国国家工程科学院院士, 德国汉堡大学教授, 多模态智能系统研究所所长，清华大学杰出访问教授

Jianwei Zhang is professor and Director of Technical Aspects of Multimodal Systems, Department of Informatics, Universität Hamburg. He is Academician of the German National Academy of Engineering Sciences and the Academy of Sciences and Humanities in Hamburg Germany. He is also Distinguished Visiting Professor of Tsinghua University. 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 multimodal information (visual, auditory, tactile, etc.) processing; cognitive sensor fusion for robot perception; real-time learning algorithms; modelling of sensory-motor control tasks; natural human-robot interaction; learning and control of robot grasping and in-hand manipulation; experience-based robot learning; best view algorithm for active robot vision; mobile manipulation service robots, etc. In these areas, he has published over 500 journal and conference papers, and holds over 50 patents of robot mechatronic design, novel robot arms and end-effectors, modular robots, etc. He is the General Chair of IEEE MFI (Multisensor Fusion and Integration) 2012, the Robotics Flagship Congress IEEE/RSJ IROS (Intelligent Robots and Systems) 2015, and HCR (Human-Centred Robotics) 2018, and Associated VP of IEEE Robotics Automation Society CAB, etc. Jianwei Zhang 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 which 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.

Speech Title:  Robust robot cognition and control driven by models and crossmodal learning
模型与跨模态休息混合驱动的机器人鲁棒认知与控制

Abstract: Robot systems are needed to solve real-world challenges by combining data-based machine learning with cognitive, kinematic, dynamic as well as physical models of cognitive abilities in intelligent systems. There has been substantial progress in crossmodal learning deep neural networks and LLMs in terms of data-driven benchmarking. However, such data-driven systems are computationally very costly and not yet interpretable, while most model-based approaches are not robust in an unstructured, dynamic, and changing world. My talk will first introduce concepts of cognitive systems that allow a robot to better understand multimodal scenarios by integrating knowledge and learning and then the necessary modules to enhance the robot intelligence level. Then I will explain how a robot can consolidate its model as a result of learning from experiences; and how such cross-modal learning methods can be realized in intelligent robots. In the end, I will demonstrate several novel robot systems with human-robot interaction, dexterous walking, and manipulation skills in potential service applications.