Prof. Dr. Naoyuki Kubota
Research Center for Community-centric Systems,
Graduate School of Systems Design, Tokyo Metropolitan University, Japan.
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| Naoyuki Kubota is currently a Professor in the Department of Mechanical Systems Engineering, the Graduate School of Systems Design, and Director of Community-centric System Research Core, Tokyo Metropolitan University, Japan. He graduated from Osaka Kyoiku University in 1992, received the M.E. degree from Hokkaido University in 1994, and received the D.E. from Nagoya University, Nagoya, Japan, in 1997. He was an Assistant Professor and Lecturer at the Department of Mechanical Engineering, Osaka Institute of Technology, Japan, from 1997 to 2000. In 2000, he joined the Department of Human and Artificial Intelligence Systems, the School of Engineering, Fukui University, Japan, as an Associate Professor. He joined the Department of Mechanical Engineering, the Graduate School of Engineering, Tokyo Metropolitan University, Japan, as an Associate Professor in 2004. He was an Associate Professor from 2005 to 2012, and a Professor from 2012 at the Graduate School of Systems Design, Tokyo Metropolitan University, Japan. He was a Visiting Professor at University of Portsmouth, UK, in 2007 and 2009, and was an Invited Visiting Professor at Seoul National University from 2009 to 2012, and others. His current interests are in the fields of topological mapping, coevolutionary computation, spiking neural networks, perception-based robotics, robot partners, and informationally structured space. He has published more than 500 refereed journal and conference papers in the above research fields. He received the Best Paper Award of IEEE IECON 1996, IEEE CIRA 1997, MHS 2011, WAC 2012, HSI 2016, and so on. He was an associate editor of the IEEE Transactions on Fuzzy Systems from 1999 to 2010, the IEEE CIS Intelligent Systems Applications Technical Committee, Robotics Task Force Chair from 2007 to 2014, IEEE Systems, Man, and Cybernetics Society, Japan Chapter Chair since 2018, Vice Director, Tokyo Biomarker Innovation Research Association, Japan from 2020, and others. |
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Topological Intelligence for Cognitive Robotics Recently, the mobility of robots has been discussed from the viewpoints of cyber-physical systems and digital twin. Especially, the study on cognitive robotics deals with the mobility based on adaptation, learning, and evolution in dynamically changing environments. In order to conduct multiscale and multiphysics simulations in such a situation, we often need to extract topological features and structures from online big data streams. Therefore, we proposed the concept of topological twin. The aim of topological twin is to (1) extract topological structures hidden implicitly in the real world, (2) reproduce them explicitly in the cyber world, and (3) simulate and analyze the real world in the cyber world. While we have to deal with the physical dynamics in the microscopic level, we have to deal with spatiotemporal qualitative knowledge in the macroscopic level. Furthermore, we need a mesoscopic integration method connecting microscopic and macroscopic topological features. In this way, the topological twin plays the important role in extracting and connecting structures hidden in real world from the mutliscopic point of view. Furthermore, we need a multiscopic approach to deal with inference, learning, search, and prediction based on topological and graphical data as the methodology of topological intelligence. In this talk, first, we introduce the concepts of cognitive robotics and multiscopic topological twin. Next, we explain various types of topological clustering methods and graph-based methods related with topological intelligence. Furthermore, we show several experimental results on topological intelligence for the mobility in robotics. Finally, we discuss the applicability and future direction of topological intelligence for cognitive robotics.
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