
Naoyuki Kubota
Tokyo Metropolitan University
Talk: Multi-scopic Topological Intelligence for Robotics
Recently, various types of robots have been developed such as mobile robots, robot partners, and mobility support robots. With the rapid progress of sensing technology, network technology, and machine learning technology, we can realize real-time measurement of people and moving objects in human-robot coexisting environments, but we need cyber-physical system integration and analysis. In order to perform multiscale and multiphysics simulations of a real-world phenomenon, we should extract and use topological features and structures from given or measured big data. Therefore, we proposed the concept of topological twin. The goal of topological twin is to (1) extract topological structures implicitly hidden in the real world, (2) explicitly reproduce them in the cyber world, and (3) simulate and analyze real-world phenomena in the cyber world. While we have to deal with the physical dynamics at the microscopic level, we have to deal with spatiotemporal qualitative relationships between objects, people, culture, and knowledge at the macroscopic level. We also need a mesoscopic integration method connecting microscopic and macroscopic topological features. Thus, the topological twin plays an important role in extracting and connecting hidden structures in real world from the multiscopic point of view. Furthermore, we need a multiscopic approach to deal with inference, learning, search, and prediction based on topological and graphical data as a methodology of topological intelligence. In this talk, we first introduce the concept of multiscopic topological twin. Next, we explain various types of topological clustering methods and graph-based methods related to topological intelligence. One of them is Growing Neural Gas (GNG), and we have proposed multi-layer GNG (ML-GNG) to reduce the computational cost of the original GNG and multi-scale batch-learning GNG to improve the convergence property and dynamic adaptation property. Next, I show experimental results of multiscopic topological intelligence for trailer living laboratory, robot partners, multi-legged robots, and mobility support robots. Finally, I discuss the applicability and future directions of multiscopic topological intelligence.
Bio: Naoyuki Kubota is currently a professor in the Department of Mechanical Systems Engineering, the Graduate School of Systems Design, and director of the Community-centric Systems Research Center, Tokyo Metropolitan University, Japan. He is the representative director of the Tokyo Biomarker Innovation Research Association, Japan. He received his Ph.D. degree from Nagoya University, Japan, in 1997. He was a Visiting Professor at University of Portsmouth, UK, Seoul National University, and others. His current interests are in the areas of topological intelligence, coevolutionary computation, spiking neural networks, robot partners, and informationally structured space. He has published more than 600 peer-reviewed journal and conference papers in the above research areas. He received the Best Paper Award of IEEE IECON 1996, IEEE CIRA 1997, and so on. He was an associate editor of the IEEE Transactions on Fuzzy Systems from 1999 to 2010, Editorial Board Member of Advanced Robotics from 2004 to 2007, Editorial Board Member of Journal of Advanced Computational Intelligence and Intelligent Informatics since 2004, the IEEE CIS Intelligent Systems Applications Technical Committee, Robotics Task Force Chair from 2007 to 2014, Editor of ROBOMECH Journal since 2012, IEEE Systems, Man, and Cybernetics Society, Japan Chapter Chair from 2018 to 2021, IEEE Transactions on Affective Computing Steering Committee Member since 2019, and others.