Speakers
Universität Hamburg
Jianwei Zhang obtained his bachelor's and master's degrees from the Department of Computer Science at Tsinghua University in 1986 and 1988, respectively. In 1994, he earned his Ph.D. in robotics from the Department of Computer Science at Karlsruhe University in Germany. He is a professor at the Department of Computer Science at the University of Hamburg, the director of the Multimodal Intelligent Technology Research Institute, an outstanding visiting professor at Tsinghua University, a foreign academician of the Chinese Academy of Engineering, and an academician of the German National Academy of Engineering.
He has been engaged in and leading research on intelligent systems, including perception, learning and planning, multi-sensor information processing and fusion, cross-modal information representation, robotic operating systems, and multimodal human-computer interaction for many years. His work provides a strong theoretical framework and computational models for applications in fields such as Industry 4.0, future mobility, rehabilitation medicine, and elder care services. Professor Zhang has led several significant research projects, including key projects funded by the German Research Foundation, projects from the Federal Ministry of Education and Research, EU ICT initiatives, and Sino-German interdisciplinary research SFB. He has also nurtured a large number of young scientists who are active in promoting Sino-German cooperation, working across cultures and disciplines, and focusing on future-oriented research.
Title: Ethics-aware Embodied AI Connecting Physical World
Abstract: General-purpose robot systems are needed to solve real-world challenges by combining data-based machine learning with physical, kinematic, dynamic, and interaction models of human-in-the-loop intelligent systems. There has been substantial progress in deep neural networks and Large Multimodal Models (LMMs) in terms of data-driven benchmarking and learning. However, acquiring large multimodal data for robots in the real world is challenging, and such data-driven systems are computationally costly and not yet interpretable. At the same time, most model-based approaches lack robustness in unstructured, dynamic, and changing environments. My talk will first introduce concepts based on findings in cognitive systems that allow a robot to better understand multimodal scenarios by integrating knowledge and learning. I will then outline the necessary modules to enhance the robot's intelligence level. Following that, I will explain how large multimodal learning methods can be realized in intelligent robots. Finally, I will demonstrate several novel robot systems with skills in dexterous manipulation, robust dynamic walking, and natural human-robot interaction, showcasing their potential for service applications for the benefits of human kind.
Lingnan University, Hongkong, China
Professor KWONG Sam Tak Wu is the Associate Vice-President (Strategic Research), J.K. Lee Chair Professor of Computational Intelligence, the Dean of the School of Graduate Studies and the Acting Dean of the School of Data Science of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions, and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong was listed as the World’s Top 2% Scientists by Stanford University since 2022 and one of the most highly cited researchers by Clarivate in 2022 and 2023. He has also been actively engaged in knowledge transfer between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding. He was a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) in 2022, and the President of the IEEE Systems, Man, and Cybernetics Society (SMCS) in 2021-23. He is a fellow of US National Academy of Inventors (NAI) and the Hong Kong Academy of Engineering Sciences (HKAES). Professor Kwong has a prolific publication record with over 350 journal articles, and 160 conference papers with an h-index of 84 based on Google Scholar. He is currently the associate editor of a number of leading IEEE transaction journals.
University of Manchester, UK
Angelo Cangelosi is Professor of Machine Learning and Robotics at the University of Manchester (UK) and co-director and founder of the Manchester Centre for Robotics and AI. He was selected for the award of the European Research Council (ERC) Advanced grant (funded by UKRI). His research interests are in cognitive and developmental robotics, neural networks, language grounding, human robot-interaction and trust, and robot companions for health and social care. Overall, he has secured over £38m of research grants as coordinator/PI, including the ERC Advanced eTALK, the UKRI TAS Trust Node and CRADLE Prosperity, the US AFRL project THRIVE++, and numerous Horizon and MSCAs grants. Cangelosi has produced more than 300 scientific publications. He is Editor-in-Chief of the journals Interaction Studies and IET Cognitive Computation and Systems, and in 2015 was Editor-in-Chief of IEEE Transactions on Autonomous Development. He has chaired numerous international conferences, including ICANN2022 Bristol, and ICDL2021 Beijing. His book “Developmental Robotics: From Babies to Robots” (MIT Press) was published in January 2015, and translated in Chinese and Japanese. His latest book “Cognitive Robotics” (MIT Press), coedited with Minoru Asada, was recently published in 2022.
Growing theoretical and experimental psychology research on action and language processing and on number learning and gestures in children and adults clearly demonstrates the role of embodiment in cognition and language processing. In psychology and neuroscience, this evidence constitutes the basis of embodied cognition, also known as grounded cognition. In robotics and AI, these studies have important implications for the design of linguistic capabilities, in particular language understanding, in robots and machines for human-robot collaboration. This focus on language acquisition and development uses Developmental Robotics methods, as part of the wider Cognitive Robotics approach. During the talk we will present examples of developmental robotics models and experimental results with the baby robot iCub and with the Pepper robot. One study focuses on the embodiment biases in early word acquisition and grammar learning. The same developmental robotics method is used for experiments on pointing gestures and finger counting to allow robots to learning abstract concepts such as numbers. We will then present a novel developmental robotics model, and human-robot interaction experiments, on Theory of Mind and its relationship to trust. This considers both people’s Theory of Mind of robots’ capabilities, and robot’s own ‘Artificial Theory of Mind’ of people’s intention. Results show that trust and collaboration is enhanced when we can understand the intention of the other agents and when robots can explain to people their decision making strategies.
The implications for the use of such cognitive robotics approaches for embodied cognition in AI and cognitive sciences, and for robot companion applications will also be discussed. The talk will also consider philosophy of science issues on embodiment and on machine’s understanding of language, the ethical issues of trustworthy AI and robots, and the limits of current big-data large language models.
Xi’an Jiaotong University
Xuguang Lan received Ph.D. degree in Pattern Recognition and Intelligent System from Xi'an Jiaotong University in 2005. Currently, he is a professor at Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. His main research areas include computer vision, robot learning, multi-agent game and and human-robot collaboration. He is the director of the "Coexisting-Cooperative-Cognitive Robot, Tri-Co Robot" Committee of the Automation Society, etc. He has published more than 100 papers in journals and conferences such as IEEE Transactions and ICML/CVPR/RSS, and has obtained more than 10 national invention patents. He is a senior member of IEEE.
Title: The Challenge of Embodied Intelligence: Causal Reasoning and Learning in the Physical World
Abstract: The talk briefly introduces the progress of AI in large models, including large language model, vision and multimodality, etc., especially the progress and challenges faced by robots in behavioral intelligence. It also explores the role of world models (imagination reasoning) in behavior. Aiming at the difficult problems, a robot autonomous manipulation method is proposed based on visual reasoning in unstructured scenes. The large language model is embeded into visual grounding, so that robots can perform visual reasoning on dynamic unstructured scenes and complete autonomous manipulations of specific objects in the best way. It further introduces the proposed a continuous reinforcement learning of robot based on experience consistency distillation, the multi-robot autonomous collaboration method guided by imagination, and related application.
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