Tutorial and Workshops

Tutorial 01
Tuesday September 6, 9:00-12:20, Room 15

Control barrier function: Fundamentals, designs and applications
Organizer:
Prof. Hisakazu Nakamura (Tokyo University of Science, Japan)
Abstract:
In recent years, control barrier functions (or its abbreviation CBF) have attracted much attention in control
systems theory and engineering for safety-critical or state-constrained control problems. This tutorial
introduces mathematical frameworks of safety assurance by using CBFs. This tutorial consists of three talks
from Prof. H. Nakamura (Tokyo University of Science, Japan), Prof. T. Ibuki (Meiji University, Japan), and Dr. S.
Kimura (Shimizu Corporation, Japan).
Prof. Nakamura introduces definitions and basic properties of CBFs and mathematical frameworks of safetyassurance. This talk defines the forward-completeness of solutions of differential equations for state-constraint
problems. Then, the definitions of two types of CBFs: reciprocal and zeroing CBFs, are introduced.
Fundamental mathematical tools for these two types of CBFs have been given; a zeroing-type CBF canguarantee safety by Nagumo's theorem, and a reciprocal-type one by Gronwall's theorem. This talkelucidates the theoretical limitations of these theorems.
Prof. Ibuki introduces design examples of control barrier functions (CBFs) for safety-assurance control. This
talk focuses on mobile robots as controlled targets for ease of understanding.
CBF design procedures are introduced for obstacle avoidance control of a single mobile robot and multi-robots.
Then, distributed safety-assurance control of networked systems is introduced for general nonlinear dynamics.
Moreover, the design of a data-driven CBF based on Gaussian processes is introduced for learning a safety set
in a workspace of mobile robots.
Finally, various types of CBFs and those applications currently presented by many researchers in robotics andcontrol communities are briefly summarized.
Dr. Kimura introduces an industry application of CBF-based control. This talk introduces the need for control
barrier functions in the construction industry and how they can be adopted into our research and development
projects.
We, as a corporation, also show how beginners can acquire the handling of control barrier functions and thesteps to implementation on actual robots. Through our experience, the usability of control barrier functions
will be mentioned.
Keywords:
control barrier function, safety critical system, human-in-the-loop system, mobile robot

Speaker:
1. Prof. Hisakazu Nakamura, (Tokyo University of Science, Japan)
Hisakazu Nakamura was born in Osaka on August 13, 1976. He received
the M.S. in control engineering from the Tokyo Institute of Technology,
Japan, in 2000 and the Ph.D. in information technology from the Nara
Institute of Science and Technology, Japan, in 2003. From 2003 to 2011,
he was an Assistant Professor at the Graduate School of Information
Science of the Nara Institute of Science and Technology. From 2008 to
2009, he was a visiting researcher at the SYSTeMS Research Group of
Ghent University in Belgium. He is a professor at the Department ofElectrical Engineering of Tokyo University of Science. He won the SICE
Young Researcher's Award in 2007, and the SICE Outstanding Paper Award in 2017.
interests include nonlinear control theory and its application to robotics.

His research

Title: Mathematical introduction to control barrier function-based safety assurance
Abstract: Recently, control barrier functions (or its abbreviation CBF) have attracted much attention incontrol systems theory and engineering for safety-critical or state-constrained control problems. Thistutorial talk aims to introduce definitions and basic properties of CBFs and mathematical frameworks ofsafety assurance.
Firstly, this talk defines the forward-completeness of solutions of differential
equations for state-constraint problems. Secondly, the definitions of two types of CBFs: reciprocaland zeroing CBFs, are introduced. Then, fundamental mathematical tools for these two types of CBFs
have been given; a zeroing-type CBF can guarantee safety by Nagumo's theorem, and a reciprocal-type
one by Gronwall's theorem. This talk elucidates the theoretical limitations of these theorems.

2.

Prof. Tatsuya Ibuki, (Meiji University, Japan)
Tatsuya Ibuki received the B.Eng., M.Eng., and Ph.D. degrees inmechanical and control engineering from Tokyo Institute of Technology in2008, 2010, and 2013, respectively. He was a research fellow with theJapan Society for the Promotion of Science from 2012 to 2013, anassistant professor with the Department of Systems and Control
Engineering, Tokyo Institute of Technology from 2013 to 2020, and avisiting scholar with the School of Electrical and Computer Engineering,
Georgia Institute of Technology in 2019. Since 2020, he has been a seniorassistant professor with the Department of Electronics and Bioinformatics,
Meiji University. His research interests include cooperative control ofrobotic networks, fusion of control theory and machine learning, andvision-based estimation and control.

Title: Design of control barrier functions for safety-assurance control of mobile robots
Abstract: This talk introduces design examples of control barrier functions (CBFs) for safety-assurance
control. In this talk, mobile robots are mainly focused on as the controlled targets for the ease ofunderstanding. First, design of a CBF for obstacle avoidance control of a single mobile robot is
introduced. Secondly, this obstacle avoidance methodology is extended to a multi-robot system case:
CBF-based distributed collision avoidance between mobile robots. The technique of distributed
safety-assurance control is also generalized for networked systems with general nonlinear dynamics.

Thirdly, design of a data-driven CBF based on Gaussian processes is introduced for learning of safetyarea in work space of mobile robots. Finally, various type of CBFs and those applications, currently
presented by many researchers in robotics and control communities, are briefly summarized.

3.

Dr. Shunsuke Kimura, (Shimizu corporation, Japan)
Shunsuke Kimura received his bachelor’s and master’s degrees inengineering from Tokyo University of Science in 2013 and 2015, and his
doctoral degree in engineering from Tokyo Institute of Technology in 2018,
respectively. He is currently a researcher at the Institute of Technology of
Shimizu Corporation. His research interests include nonlinear control
theory, mobile robots, human robot interaction, and robot management
systems. He is a member of IEEE, RSJ and SICE.

Title: Application examples of control barrier functions in the construction industry
Abstract: In recent years, the theoretical development of control barrier functions has attracted muchattention. On the contrary, there are still few application examples of control barrier functions in industry.
This talk introduces the needs of control barrier functions in the construction industry and how they canbe adopted into our research and development projects. We, as a corporation, also show how beginners
can acquire the handling of control barrier functions and the steps to implementation on actual robots.
Through our experience, the usability of control barrier functions will be mentioned.
Program:
9:00–10:00
10:00–10:10
10:10–11:10
11:10–11:20
11:20–12:20

Mathematical introduction to control barrier function-based safety assurance
Hisakazu Nakamura (Tokyo University of Science, Japan)
Break
Design of control barrier functions for safety-assurance control of mobile robots
Tatsuya Ibuki (Meiji University, Japan)
Break
Application examples of control barrier functions in the construction industry
Shunsuke Kimura (Shimizu corporation, Japan)

Tutorial 02
Tuesday September 6, 13:30-17:40, Room 15

New Trends in Machine Learning for Science and Engineering
Organizer:
Prof. Ichiro Takeuchi, (Nagoya University, Japan), Prof. Hiroyuki Okada (Nagoya University)
Abstract:
Machine learning and deep learning technologies have become crucial tools in engineering and science dueto their ability to analyze large and complex datasets. They have revolutionized many fields, including robotics,

healthcare, science, and more, enabling researchers to discover new insights, make predictions, and improve
decision-making processes. This tutorial session will cover three different topics related to artificial intelligenceand machine learning from Prof. Matthias Rätsch, (Reutlingen University, Germany), Prof. Takashi Matsubara,(Osaka University, Japan) and Prof. Ichiro Takeuchi, (Nagoya University, Japan). Overall, this tutorial session
will showcase how AI and machine learning can be applied to various fields and industries.
The first talk from Prof. Rätsch introduces research of ‘Visual Systems for Intelligent Robots’ (ViSiR) group andhow large language models(LLMs) come close to super intelligence for the AI alignment problem. Practicalexamples for the human-robot-collaboration in RT-Lions team, wining world championships in RoboCup, isshown with the collaborations with big industrial partners.
The second talk from Prof. Matsubara discusses "geometric scientific machine learning," a technique that
restricts deep learning to a subspace ensuring adherence to the laws of physics, making it suitable for rigorous
fields such as natural science and industrial applications.
The third talk from Prof. Takeuchi introduces the machine learning-based data analysis for the hypothesis
exploration and generation by effectively combining Bayesian experimental designs and deep generativemodels, specifically in the field of chemical compound designs.
Keywords:
Intelligent Systems, Computational Intelligence, Innovative Systems Approach for Realizing Smarter World
Speaker:
1. Prof. Dr. rer. nat. Matthias Rätsch, (Reutlingen University, Germany)
Prof. Rätsch is a professor at the Reutlingen University for Artificial
Intelligence and Interactive Mobile Robots since 2013. He is the head of the
Visual Systems and Intelligent Robots group (www.ViSiR.org). In 2008, hereceived his Ph.D. degree in the graphics and vision research group(GraVis) at the University of Basel, Switzerland in 3DMM face analysis. Until
2013 he was with the world-leading company Cognitec Systems for facerecognition. His research interests are Image Understanding, Computer
Vision, Artificial Intelligence, Deep Learning, Edge-AI, Autonomous Driving,
Humanoid Robots, Bionic Intelligence and Grasping, Human Robot
Collaboration, Large Language Models, Intelligent ChatBots, and AI-Ethics. He is the head of the
RoboCup team RT-Lions (www.rt-lions.de). The team was World Champion in 2009 and German Masterin 2010, after changing to the RoboCup@Home league, the team could win the Portuguese Open and
the SICK Robot Day, and joined the World Championship in Nagoya, Japan and in Sydney, Australia.
Recently the team obtained the 3rd Prize at World Championship Worldwide Virtual. He was a memberof the program committee and session chair for several international conferences and invited speeches
in Artificial Intelligence, Face Analysis, and Robot Vision. He published more than 50 international
academic research papers and journals, like at IEEE Transactions on Image Processing or at the IEEE
SIGGRAPH conference. He was leading the 1.1 Mill € founded industrial project “KollRo 4.0” (with BMBF,BOSCH, Mercedes Benz), two ZIM projects (0.4 Mill). He leads current the ProFö “BInGO” and theInvestBW “FAIR” projects (0.6 Mill €), and a further 2.4 Mill € are proposed. He joined several fundedindustrial projects, like RTMO (BMBF), GES 3D (BMBF), Face-HMI (SAB, COG), and I-Search (BMBF).

Title: A Tutorial how to Train Humanoid Robots with Artificial Super Intelligence. Are they
the End or the Last Hope for Humans?
Abstract: Recent research in artificial intelligence and robotics, termed as fourth industrial or robot
revolution, is changing our world. In this tutorial I will introduce research of our group ‘Visual Systems forIntelligent Robots’ (ViSiR). We will discuss, how LLMs (like ChatGPT) come close to super intelligence,taking over power, if we cannot solve the alignment problem. On practical examples we will learn to train
an AI for human-robot-collaboration. All is illustrated on robots of our RT-Lions team, wining world
championships in RoboCup. Practical examples are shown from collaborations with strong industrial
partners, like BMW, Mercedes Benz, BOSCH, FESTO or KUKA
2.

Prof. Takashi Matsubara, (Osaka University, Japan)
Takashi Matsubara was born in Nara, Japan in 1988. He received his B.E.,
M.E., and Ph.D. degrees from Osaka University, Osaka, Japan, in 2011,2013, and 2015, respectively. From 2015 to 2020, he worked as an
assistant professor at the Graduate School of System Informatics, Kobe
University, Hyogo, Japan. Since 2020, he is serving as an associate
professor at the Graduate School of Engineering Science, Osaka University,
Osaka, Japan. In 2021, he was appointed as a researcher under the Japan
Science and Technology Agency (JST) PRESTO program. He also serves
as an editorial committee member for the Journal of the Japanese Society
for Artificial Intelligence (JSAI) and as a representative for the Institute ofElectronics, Information and Communication Engineers (IEICE). His
research interests focus on integrating inductive bias into deep learning fordata-driven modeling. In 2021, his research was awarded the Strategic Information and Communications
R&D Promotion Program (SCOPE) Research Encouragement Award by the Ministry of Internal Affairs
and Communications, Japan.

Title: Geometric Deep Learning for Modeling Dynamical Systems and Incorporating Laws ofPhysics
Abstract: Artificial intelligence has made remarkable strides due to deep learning, which is an inductive
method that employs data to model a system. However, it struggles in rigorous fields such as naturalscience and industrial applications, where deductive approaches like scientific computing are morecommonly used. They can gain insights into the mathematical structures underlying systems anddevelop computational methods to preserve them. In this talk, I will introduce "geometric scientificmachine learning," which incorporates such a deductive approach into artificial intelligence. This
technique restricts the function space of deep learning to a subspace that guarantees adherence to thelaws of physics while learning dynamics from data.
3.

Ichiro Takeuchi, (Nagoya University, Japan / RIKEN)
Ichiro Takeuchi is a professor at Nagoya University in Japan and leads ateam at the RIKEN Center for Advanced Intelligence Project. He earned
his B.Eng., M.Eng., and D.Eng. degrees from Nagoya University in 1996,
1998, and 2000, respectively. After serving as a post-doctoral researcher
under Prof. Yoshua Bengio in Montreal, Canada, he became a tenured
assistant professor at Mie University in 2001. He later achieved associate
and full professor positions at Nagoya Institute of Technology in 2008 and2015, respectively, and finally a full professor position at Nagoya
University in 2022. His research focuses on machine learning theory andalgorithms and their applications in bio-medical and material sciences. He has published numerous
studies in top machine learning and data mining conferences such as ICML, NIPS, and KDD, as well as
high-impact journals in the respective fields.

Title: Data-driven Exploration and Generation of Scientific Hypotheses by Deep Generative
ModelsAbstract: Data-driven science is a rapidly growing field that aims to use data collection and analysis todrive scientific research. At the core of this field is the use of machine learning-based data analysis to
explore and generate new hypotheses, which can lead to new scientific discoveries. In this tutorial, I will
delve into the recent advances in hypothesis exploration and generation by effectively combining
Bayesian experimental designs and deep generative models. These methods have been shown to beparticularly promising in uncovering new insights and understanding complex systems. We will discuss
the underlying principles of these methods and explore their applications in the field of chemicalcompound designs.
Program:
13:30–15:00

15:00–15:10
15:10–16:20

16:20–16:30
16:30–17:40

A Tutorial how to Train Humanoid Robots with Artificial Super Intelligence. Are they
the End or the Last Hope for Humans?
Matthias Rätsch (Reutlingen University, Germany)
Break
Geometric Deep Learning for Modeling Dynamical Systems and Incorporating Laws ofPhysics
Takashi Matsubara (Osaka University, Japan)
Break
Data-driven Exploration and Generation of Scientific Hypotheses by Deep Generative
ModelsIchiro Takeuchi (Nagoya University, Japan)

Workshop 1
Tuesday September 6, 9:00-12:20 and 13:30-17:25, Room 24

Complex Networks on Time-Series for Process Analysis
Organizers:
Arun K. Tangirala, (Dept. of Chemical Engineering, IIT Madras)
Lakshminarayanan Samavedham, (Dept. of Chemical and Biomolecular Engineering, National University ofSingapore)
Abstract:
Graphical representations or network models have found increasing applications in linear / non-linear complex
dynamical systems, for the purposes of characterising and discovering changes in structural properties ofsignals / systems, causal discovery, analysis and control of complex systems, root-cause diagnosis, epidemic
spread, brain connectivity, etc. Network models have a few key advantages: (i) a suite of simple (coarse) tofine-grained dynamical relationships can be constructed (ii) no prior distinction between causes and effects are
required (iii) can be easily extended to accommodate univariate, multivariable signals and system of systems
and (iv) the availability of a graph theory formalism.
The workshop aims to present a few well-established and emerging methods for reconstructing networkmodels from time-series data. Specifically, we shall focus on identifying three different classes of networks orgraphs: visibility graphs, correlation / coherence networks and causal networks for deterministic and stochastic
processes. Applications of these network models in engineering and other domains shall be presented. Further,a brief discussion on experimental design for network identification is also included. The focus is on providing

the theory and tools for developing network models that (i) capture structural dependencies in non-linear
time-series (ii) graphically represent directional and undirected dependencies and (iii) model non-linear
dynamical systems.
Keywords:
Modeling, System Identification and Estimation; Computational Intelligence; Signal and/or Image Processing

Program:Complex Networks on Time-Series for Process Analysis I
9:00–10:05
10:05–10:15
10:15–11:00
11:00–12:00
12:00–13:30

Network (Graphical) Models for Dynamical Systems Overview and Essentials
Arun K. Tangirala (Indian Institute of Technology Madras, India)
Break
Correlation and Coherence Networks
Arun K. Tangirala (Indian Institute of Technology Madras, India)
Granger Causality and GC Networks
Arun K. Tangirala (Indian Institute of Technology Madras, India)
Lunch breakComplex Networks on Time-Series for Process Analysis II
13:30–14:15
14:15–15:45

15:45–15:55
15:55–16:40
16:40–17:25

Deterministic Causal Networks using PCA
Arun K. Tangirala (Indian Institute of Technology Madras, India)
CCM-Causal Networks for Non-linear Systems
Lakshminarayanan Samavedham (National University of Singapore) and Arun K. Tangirala(Indian Institute of Technology Madras, India)
Break
Experimental Design for Network Identification
Lakshminarayanan Samavedham (National University of Singapore)
Applications
Lakshminarayanan Samavedham (National University of Singapore) and Arun K. Tangirala(Indian Institute of Technology Madras, India)

Workshop 2
Tuesday September 6, 9:00-12:00, Room 25

Stochastic Configuration Networks for Industrial Data Analytics
Organizer:
Prof. Dianhui Wang, (China University of Mining and Technology, Northeastern University, China)
Abstract:
Recently, we developed a new randomized learning algorithm and ensure the resulting model, termed
Stochastic Configuration Networks (SCNs), holds the universal approximation property. Such a milestone
progress greatly contributes to both the advancement of knowledge on randomized learning theory and thedevelopment of lightweight computing technology for IoT-based industrial applications. This workshop aims tointroduce the state-of-the-art of SCN models, algorithms and applications to the instrument and control
community in Japan.

Speakers:
Dr Wang was awarded a Ph.D. from Northeastern University, Shenyang, China, in 1995.
From 1995 to 2001, he worked as a Postdoctoral Fellow at Nanyang Technological
University, Singapore, and a Researcher at The Hong Kong Polytechnic University, Hong
Kong, China. From July 2001 to December 2020, he worked as a Reader in the
Department of Computer Science and Information Technology, La Trobe University,
Australia, and with adjunct appointment from 2021. Since 2017, Dr Wang has been avisiting Professor at State Key Laboratory of Synthetical Automation of Process Industries,
also Industrial AI Research Institute, Northeastern University, China. In July 2021, He
joined AI Research Institute at China University of Mining and Technology, working as aDean, Professor, and Director of Research Center for Stochastic Configuration Machines. His current research
focuses on industrial artificial intelligence, specifically on Deep Stochastic Configuration Networks(http://www.deepscn.com/) for data analytics in process industries, intelligent sensing, and control systems,
prediction of significant and small probability events. Dr Wang published more than 240 technical papers on
applied mathematics, control engineering and computer sciences. Dr Wang is a Senior Member of IEEE,
serving as an Associate Editor for IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems,
Information Sciences, Artificial Intelligence Review and WIREs Data Ming and Knowledge Discovery.

Dr. Qiao received the B.E. and M.E. degrees in control engineering from Liaoning
Technical University, Fuxin, China, in 1992 and 1995, respectively, and the Ph.D. degreefrom Northeast University, Shenyang, China, in 1998. From 1998 to 2000, he was a
Postdoctoral Fellow with the School of Automatics, Tianjin University, Tianjin, China. He
joined the Beijing University of Technology, Beijing, China, where he is currently aprofessor. He is also the Director of the Intelligence Systems Laboratory, Beijing. His
current research interests include neural networks, intelligent systems, self-adaptive
learning systems, and process control systems. Prof. Qiao is a member of the IEEE
Computational Intelligence Society.

Dr Ding received the B. E., M.E. and Ph.D. degrees in Control Theory and Control
Engineering from Northeastern University, Shenyang, China. He is a Professor of theState Key Laboratory of Synthetical Automation for Process Industry, Northeastern
University. He has authored or co-authored over 200 refereed journal papers and refereed
papers at international conferences. He has also invented or co-invented over 50 patents.
His research interests include modeling, plant-wide control and optimization for thecomplex industrial systems, machine learning, industrial artificial intelligence, andcomputational intelligence and application. Prof. Ding was a recipient of the Young
Scholars Science and Technology Award of China in 2016, the National Science Fund forDistinguished Young Scholars in 2015, the National Technological Invention Award in 2013, and threeFirst-Prize of Science and Technology Awards of the Ministry of Education in 2006, 2012, and 2018,
respectively.

Dr Dai received his master’s and Ph.D. degrees in control theory and control engineeringfrom Northeastern University, China, in 2009 and 2015, respectively. He is working as a
Professor of the Artificial Intelligence Research Institute, China University of Mining andTechnology (CUMT), China. His research focuses on industrial big data analytics, industrial
artificial intelligence, and knowledge discovery for complex industrial processes. He has
been awarded with the National Youth Talent Support Program, and has received the First
Prize of Liaoning Province Patent Award in 2022, the Youth Science and Technology
Award of China Coal Industry in 2022. Prof. Dai is an Associate Editor of Industrial Artificial
Intelligence and an Editorial board member of the Frontiers in Control Engineering.

Dr Zhang received the B.Sc. degree in applied mathematics, the M.S. degree in control
theory and control engineering, and the Ph.D. degree in control theory and control
engineering from Northeastern University, China, in 1989, 2004, and 2008, respectively,
where he is currently with the College of Sciences. He has published more than 60 SCI
journal papers and 150 EI papers and 3 books (including Fundamentals of control theory inHigher Education Press, National Eleventh Five Year Plan textbook). His researchinterests include fuzzy control, fractional order control systems and singular systems. He
won the First prize of teaching achievement of Liaoning Province and the excellent teacher
of Northeastern University. Prof. Zhang is an Associate Editors of IEEE Access and IET
Electronic Letters.

Program:
9:00–9:40

9:40–10:20
10:20–10:30
10:30–11:00
11:00–11:30
11:30–12:00

Stochastic Configuration Networks for Industrial Artificial Intelligence
Dianhui Wang, (CUMT (Xuzhou) and NEU (Shenyang) China
Dynamic Stochastic Configuration Networks with Lifelong Learning for Industrial Data
Stream Modelling
Junfei Qiao, Beijing University of Technology, Beijing, China
Break
Stochastic Configuration Networks for Prediction of Industrial Production IndicesProf. Jinliang Ding, Northeastern University, Shenyang, China
Stochastic Configuration Networks for Human Activity Recognition
Prof. Wei Dai, China University of Mining and Technology, China
Universal Approximation Property of Stochastic Configuration Networks for Time
SeriesProf. Xuefeng Zhang, Northeastern University, Shenyang, China