Plenary and Semi-Plenary Sessions

Plenary Sessions

Plenary Talk I:

Abstract: Large models based on internet-scale data can now pass the Turing Test for intelligence. In this sense, data has "solved" language and many analogously claim that data has solved speech recognition and computer vision. Will data also solve robotics and automation, allowing general-purpose humanoid robots to achieve human-level performance?

Using commonly accepted metrics for converting word and image tokens into time, the amount of internet-scale data used to train contemporary large vision language models (VLMs) is on the order of 100,000 years. I'll review 3 ways researchers are pursuing to close this gap, and a 4th approach, where data is collected as real robots operate in real commercial environments – which requires bootstrapping with AI and "good old-fashioned engineering" to create robots with real return on investment that will be adopted by industry. Such robots can create a "data flywheel" to increase performance and enable new functionality, accelerating the timeline to achieve reliable, general-purpose robots.

Biography: Ken has been interested in robots, rockets, and rebels since he was a kid. He's skeptical about claims that humans are on the verge of being replaced by Superintelligent machines yet optimistic about the potential of technology to improve the human condition. Ken developed the first provably complete algorithm for part feeding and the first robot on the Internet. In 1995 he was awarded the Presidential Faculty Fellowship and in 2005 was elected IEEE Fellow: "For contributions to networked telerobotics and geometric algorithms for automation."Ken founded UC Berkeley's Art, Technology, and Culture public lecture series in 1997 serves on the Advisory Board of the RoboGlobal Exchange Traded Fund. Ken is Chief Scientist at Ambidextrous Robotics and on the Editorial Board of the journal Science Robotics. He served as Chair of the Industrial Engineering and Operations Research Department and co-founded the IEEE Transactions on Automation Science and Engineering. Short documentary films he co-wrote were selected for Sundance and one was nominated for an Emmy Award. He lives in the Bay Area and is madly in love with his wife, filmmaker and Webby Awards founder Tiffany Shlain, and their two daughters.

Plenary Talk II:

Abstract: Optimal control and filtering problems have a rich history that can be traced back to Queen Dido’s solution to the isoperimetric problem in 814 BC, the solution to the brachistochrone problem by Johann Bernoulli in 1696, and the introduction of the least-squares method by Legendre and Gauss in the late 18th and early 19th centuries.

Modern solutions exploit the calculus of variations and its control perspectives, Pontryagin’s Minimum Principle, Bellman’s Principle of Optimality, and the Wiener and Kalman filters.

While for linear systems all roads lead to the Algebraic Riccati Equation and its dual, we demonstrate that for nonlinear systems one can go well beyond the HJB equation by judiciously exploiting properties of the stable invariant manifold, the associated invariant distribution, and adapted coordinates for the underlying Hamiltonian system. These, in turn, yield PDEs that differ dramatically from the HJB equation and have a deep geometrical meaning.

Finally, we propose an ansatz for developing global solutions based on a fixed-point characterization of optimal feedback strategies and on a geometric relation between the tangent space to the stable manifold and the first order approximation of the Hamiltonian vector field away from its equilibrium.

This is joint work with Mario Sassano (University of Rome Tor Vergata).

Biography: Alessandro Astolfi (IFAC Fellow, FIEEE, Academia Europaea, FIET, ITATEC, ACA Fellow, EUCA Fellow) received the Laurea in Electronic Engineering from the University of Rome La Sapienza, Italy, in 1991; the M.Sc. degree in Information Theory and the Ph.D. degree with Medal of Honor with a thesis on discontinuous stabilization of holonomic systems from ETH-Zurich, Switzerland, in 1995; and the Ph.D. degree for his work on nonlinear robust control from the University of Rome “La Sapienza” in 1996.

From 1992 to 1996 he was a Research Associate at ETH-Zurich. From 1996 to 2026 he was with the Electrical and Electronic Engineering Department, Imperial College London. In particular, he was Professor of Nonlinear Control Theory; College Consul for the Faculty of Engineering and Business School (2022-2025); and Head of the Control and Power Group (2010-2022). He retains a part-time appointment as Professor of Nonlinear Control Theory within the Dyson School of Design Engineering at Imperial College London. From 1998 to 2003 he was an Associate Professor with the Department of Electronics and Information, Politecnico of Milano. From 2005 to 2025 he was a Professor with the Dipartimento di Ingegneria Civile e Ingegneria Informatica, University of Rome Tor Vergata, Rome. Since February 2026, he is a Professor of Applied Mathematics and Computational Science at KAUST, Saudi Arabia.

His research interests include mathematical control theory and control applications, with special emphasis for the problems of discontinuous stabilization, robust and adaptive control, observer design, optimal control, game theory, and model reduction.

Dr. Astolfi was the recipient of the IEEE CSS A. Ruberti Young Researcher Prize (2007); the IEEE RAS Googol Best New Application Paper Award (2009); the IEEE CSS George S. Axelby Outstanding Paper Award (2012); the Automatica Best Paper Award (2017); and the IEEE Transactions on Control Systems Technology Outstanding Paper Award (2023). He is a “Distinguished Member” of the IEEE CSS and a recipient of the IFAC Outstanding Service Award (2017). He is the recipient of the Institute of Measurement and Control Sir Harold Hartley Medal for “outstanding contributions to the technology of measurement and control”.

He was Editor-in-Chief of the IEEE Transactions on Automatic Control (2018–2025) and Chair of the IEEE CSS Conference Editorial Board (2010–2017). He is Vice Chair of the IFAC Technical Board (2020–2026); and a Member of the IEEE Thesaurus Editorial Board (2025–) and of the IEEE PRAC (2026–).

Plenary Talk III:

Abstract: The acrobatic feats of recent humanoids notwithstanding, today’s robots still struggle with everyday tasks like opening jars, inserting plugs, using scissors, and other manipulation tasks that humans perform effortlessly. While hardware limitations are partly to blame — today’s robot hands still lack the strength, precision, flexibility, and sensing capabilities of human hands — it is becoming increasingly clear that traditional model-based control methods are also inadequate. Manipulation involves the coordinated control of motion, force, and compliance under uncertainty, disturbances, unmodeled dynamics, and physical constraints imposed by the task and environment. During manipulation a robot may shift between open- and closed-chain systems, and between underactuated and overactuated regimes.

Data-driven methods offer a promising alternative to manually engineering such complex control laws. However, current approaches based on Vision-Language-Action (VLA) models have major shortcomings: they are often device-dependent, require enormous training data, scale poorly, and struggle to generalize across tasks. More fundamentally, they fail to fully leverage the extensive body of knowledge accumulated in control systems design and the mechanics of manipulation.

This talk proposes a new control architecture for robot manipulation that merges key concepts from control theory, mechanics, geometry, and human motor control with data-driven methods. We show that Brockett's motion description language (MDL) paradigm provides a device-independent, hierarchical, and modular framework for manipulation expressed in the language of control systems. At the highest level, foundation models decompose complex tasks into subtasks, which are then encoded as sequences of robot action primitives using both state-space control and data-driven learning methods. At lower levels, we show how coordinate-invariant geometric methods can be used to construct minimum distortion latent space manifolds, equivariant learning models, and minimum attention feedforward-feedback control laws.

Biography: Frank C. Park is professor of mechanical engineering at Seoul National University with joint appointment in the SNU Graduate School of AI. His research interests lie at the intersection of mathematical systems theory, machine learning and mathematical data science, and robotics, with differential geometry serving as a common thread. He received degrees from MIT (BS EECS) and Harvard University (PhD applied mathematics, supervised by Roger Brockett), is a fellow of the IEEE, and a member of the Korean Academy of Science and Technology and the Korean National Academy of Engineering. He is former president of the IEEE Robotics and Automation Society, former editor-in-chief of the IEEE Transactions on Robotics, and author of Modern Robotics (2017 Cambridge Univ Press, with K. Lynch) and Dynamics of Mechanical Systems (2025 Cambridge Univ Press, with K.-M. Park). He is co-founder and CEO of Saige, an industrial AI company specializing in automation and quality control.

Plenary Talk IV:

Abstract: Learning and adaptation are fundamental to the modeling and control of uncertain dynamical systems, and they are also cornerstones of intelligent systems. While statistical learning has underpinned much of the progress in machine learning, its theoretical foundations often rest on idealized assumptions about data, such as independent and identically distributed (i.i.d.) samples. These assumptions are typically not valid for dynamical systems, especially those under feedback control, where the input-output data exhibit strong temporal correlations and inherent nonstationarity. This motivates the development of a more general theoretical framework that can account for the complex data characteristics intrinsic to dynamical systems. This talk addresses these challenges by presenting some of our recent advances in learning and adaptation for stochastic dynamical systems. We introduce general and verifiable data conditions that can guarantee global stability and estimation performance for adaptive learning and filtering algorithms with either diminishing or non-diminishing adaptation gains. Topics covered include gradient-based and Newton-type adaptive methods, distributed learning and filtering in networked systems, integrated offline-online learning frameworks. We also consider practical challenges such as saturated or limited-value measurements, finite data size, input-state constraints, and time-varying system parameters. The lecture concludes with a discussion of emerging research at the nexus of control and AI.

Biography: Lei Guo received his Bachelor's degree in Mathematics from Shandong University in 1982 and his Ph.D. in Control Theory from the Chinese Academy of Sciences (CAS) in 1987. He is currently a professor at the Academy of Mathematics and Systems Science, CAS, and serves as the director of the National Center for Mathematics and Interdisciplinary Sciences, CAS. He is a Fellow of IEEE, a Member of CAS, a Foreign Member of the Royal Swedish Academy of Engineering Sciences, a Fellow of the Third World Academy of Sciences (TWAS), and a Fellow of IFAC. He was awarded the IFAC Congress Young Author Prize in 1993, the IFAC Outstanding Service Award in 2011, an honorary doctorate from the Royal Institute of Technology (KTH, Sweden) in 2014, and the Hendrik W. Bode Lecture Prize from the IEEE Control Systems Society (CSS) in 2019. He has served as President of the Asian Control Association (ACA), President of the China Society for Industrial and Applied Mathematics (CSIAM), General Co-Chair of the 48th IEEE CDC, and Distinguished Lecturer of both IFAC and IEEE-CSS. His research interests include stochastic systems, system identification, machine learning, adaptive control, adaptive filtering, adaptive game theory, control of uncertain nonlinear systems, feedback capability, multi-agent systems, and game-based control systems.

Plenary Talk V:

Abstract: Control systems are traditionally designed using continuous-time or time-triggered sampled signals, where information is encoded in amplitudes. Recently, event-triggered control has shown that control actions can be generated asynchronously and only when needed, thereby improving efficiency in networked and embedded implementations. Neuromorphic control, inspired by the functioning of biological neurons, pushes this paradigm to its extreme: control signals consist of sequences of fixed-amplitude spikes, so information is encoded entirely in the timing of spiking events.

In this talk, we address the fundamental question of how feedback controllers can be designed when the only control freedom lies in spike timing. We present a framework in which neuromorphic controllers are modeled as hybrid systems, combining continuous evolution with discrete state jumps induced by spikes. Based on this viewpoint, we discuss complementary design approaches, including Lyapunov-based triggering strategies and emulation-based constructions that approximate continuous feedback laws. Together, these results provide building blocks toward a design theory for spiking control systems.

We illustrate these design ideas through a nuclear fusion application, where plasma fueling through pellet injection yields inherently impulsive actuation, making neuromorphic feedback strategies highly relevant. The talk concludes with an outlook on this exciting research area.

Biography: Maurice Heemels received M.Sc. (mathematics) and Ph.D. (control theory) degrees (summa cum laude) from the Eindhoven University of Technology (TU/e) in 1995 and 1999, respectively. From 2000 to 2004, he was with the Electrical Engineering Department, TU/e, as an assistant professor, and from 2004 to 2006 with the Embedded Systems Institute (TNO-ESI) as a research fellow. Since 2006, he has been with the Department of Mechanical Engineering, TU/e, where he is currently a Full Professor and Vice-Dean. Maurice held visiting professor positions at Swiss Federal Institute of Technology (ETH), Switzerland (2001), University of California at Santa Barbara (2008) and University of Lorraine, France (2020). His current research includes hybrid and cyber-physical systems, networked and event-triggered control systems and model predictive control. Dr. Heemels served/s on the editorial boards of Automatica, Nonlinear Analysis: Hybrid Systems, Annual Reviews in Control, and IEEE Transactions on Automatic Control. He was a recipient of a personal VICI grant awarded by NWO (Dutch Research Council) and an ERC Advanced Grant (European Research Council). He is a Fellow of IEEE, EUCA and IFAC. He was the recipient of the 2019 IEEE L-CSS Outstanding Paper Award and the 2020 Automatica Paper Prize Award. He was in the IEEE-CSS Board of Governors (2021-2023) and the chair of the IFAC Technical Committee on Networked Systems (2017-2023). He is the Editor-in-Chief of the IFAC journal Nonlinear Analysis: Hybrid Systems since 2023.

Semi-Plenary Sessions

Semi-Plenary Talk I-1:

Abstract: Inverse reinforcement learning (IRL) aims to infer an underlying reward function from expert demonstrations, thereby eliminating the need for manually crafted rewards required in conventional reinforcement learning (RL). From a control-theoretic perspective, IRL can be viewed as a modern formulation of the inverse optimal control problem originally posed by Kalman. Despite substantial progress in recent years, several fundamental challenges must be addressed before IRL can be reliably deployed in real-world systems. These include robustness to noisy or suboptimal demonstrations, efficient learning from limited or low-quality data, and the incorporation of safety constraints in practical implementations.

In this talk, we present a control- and optimization-based framework to address these challenges. First, we introduce a differential dynamic programming (DDP)-based IRL approach for reward learning from expert demonstrations and develop a closed-loop loss formulation to improve robustness against noise in the demonstrations. Second, to enhance data efficiency, we propose online and model-free IRL algorithms that adaptively refine the reward function using real-time data. We further discuss the interplay among safety, reinforcement learning, and inverse optimality, and analyze the robustness properties of safe IRL-induced controllers. Finally, we demonstrate the application of IRL to active perception, where sensing, estimation, and control are tightly integrated through a hybrid MPC-IRL architecture.

Biography: Lihua Xie received his PhD degree from the University of Newcastle, Australia in 1992. He is currently Professor and President's Chair in Control Engineering with the School of Electrical and Electronic Engineering, Nanyang Technological University and Director, National Research Foundation Center for Advanced Robotics Technology Innovation (CARTIN). He has served as Head of Control and Instrumentation Division and Director of Delta-NTU Corporate Laboratory for Cyber-Physical Systems. His research areas include robust control, multi-agent systems, learning-based control, and unmanned systems. He has authored and co-authored hundreds of research articles, 11 monographs and over 20 patents and technical disclosures. He is currently an Editor-in-Chief of Unmanned Systems and has served as Editor of IET Book Series on Control and Associate Editor of IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Control Systems Technology, IEEE Control System Magazine, etc. He has also served as an IEEE Distinguished Lecturer, the General Chair of the 62nd IEEE Conference on Decision and Control, and a Vice-President of IEEE Control System Society. Professor Xie is Fellow of IEEE, IFAC, CAA, and Academy of Engineering Singapore.

Semi-Plenary Talk I-2:

Abstract: Optimization, control, and systems engineering solve many of our world's most complex challenges, boosting the global economy. This talk explores the immense power and untapped potential of these tools to have a positive, visible impact on our world.

Advancing industrial engineering and operations research through societally relevant applications has been the central theme of Dr. Laura Albert's academic career. In this talk, she will explore the boundless possibilities that industrial engineering and operations research offer as well as the latest trends shaping the future of the field. From emergency response and public safety to critical infrastructure protection and election resilience, she will share stories of how technical rigor translates into policy impact. Attendees will gain insight into identifying problems worthy of study, overcoming modeling challenges, creating data-driven modeling frameworks, and influencing policy.

The 21st century presents a new frontier of "wicked" problems, ranging from global supply chain disruptions to the ethical integration of artificial intelligence into complex industrial systems. This community is uniquely positioned to tackle these challenges. This talk will explore how our tools can make a positive impact on the world and provide lasting benefits for human flourishing.

Biography: Laura Albert, Ph.D., is a Professor of Industrial & Systems Engineering (ISyE) at the University of Wisconsin-Madison. She served as the President of the Institute for Operations Research and the Management Sciences (INFORMS) in 2023 and the David H. Gustafson Department Chair of ISyE at UW-Madison from 2021-24. She is the founder of Industrial and Systems Engineering Day, celebrated annually on September 14. Dr. Albert's research focuses on mitigating risks in the public sector through optimization and operations research methodologies, with applications in critical infrastructure protection, public safety, and emergency response. She is a Fellow of the American Association for the Advancement of Science (AAAS) and the Institute of Industrial and Systems Engineers (IISE). Her work has been recognized with the INFORMS Impact Prize, a National Science Foundation CAREER award, and a Fulbright Award. An advocate for public engagement, Dr. Albert has penned several op-eds for national news outlets, and she authors the engineering blogs "Punk Rock Operations Research" and "Badger Bracketology."

Semi-Plenary Talk II-1:

Abstract: Prediction—the ability to anticipate the effect of control actions from available information—is a fundamental step in control design. When prediction must be performed under limited information, uncertainty plays a central role. In this talk I will discuss how the language of Bayesian statistics provides a natural framework for establishing a separation principle between data and control. This viewpoint clarifies the role of models and reveals connections between classical model-based approaches and stochastic behavioral perspectives. Focusing on predictive control frameworks, we revisit the open-loop versus closed-loop design issue and show how System-Level Synthesis provides a computationally attractive route from data to predictive control design.

Biography: Alessandro Chiuso (Fellow, IEEE) is Professor with the Department of Information Engineering, Università di Padova. He received the "Laurea" degree summa cum laude in Telecommunication Engineering from the University of Padova in July 1996 and the Ph.D. degree (Dottorato di ricerca) in System Engineering from the University of Bologna in 2000. He has been a visiting scholar with the Dept. of Electrical Engineering, Washington University St. Louis and Post-Doctoral fellow with the Dept. Mathematics, Royal Institute of Technology, Sweden. He joined the University of Padova as an Assistant Professor in 2001, Associate Professor in 2006 and then Full Professor since 2017. He has served as an Associate Editor for Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, European Journal of Control and MCSS. He is currently serving as senior editor (System Identification and Filtering) for Automatica and chair of the IFAC Coordinating Committee on Signals and Systems. He has been General Chair of the IFAC Symposium on System Identification, 2021 and he is a Fellow of IEEE (Class 2022). His research interests are mainly at the intersection of Machine Learning, System Identification and Control.

Semi-Plenary Talk II-2:

Abstract: Extremum Seeking Control (ESC) is a real-time optimisation technique for steering static or dynamic systems toward the extremum of an unknown performance map. Since its introduction in 1922, ESC has evolved from predominantly model-free or data-driven schemes into a theoretically grounded framework with successful applications in energy systems, process control, and robotics. This talk briefly reviews the core principles of classical ESC, highlighting the inherent limitation of slow convergence in data-driven approaches, and then focuses on recent advances in model-guided extremum seeking control, where partial system knowledge is incorporated to improve transient performance and accelerate convergence while retaining robustness to uncertainty. The approach is illustrated through an application to human–prosthetic interfaces, demonstrating how model-guided ESC can efficiently optimise human–device interaction dynamics, enhance functional performance, and improve user experience, underscoring its potential for complex, uncertain, and human-in-the-loop control systems.

Biography: Dr. Ying Tan is a Professor of Mechanical Engineering at The University of Melbourne, Australia. She earned her bachelor's degree from Tianjin University, China, in 1995, and her PhD from the National University of Singapore in 2002. Following a postdoctoral fellowship at McMaster University, she joined The University of Melbourne in 2004. Dr. Tan has received numerous prestigious recognitions, including an Australian Postdoctoral Fellowship (2006–2008) and an ARC Future Fellowship (2009–2013). She currently serves on the ARC College of Experts (2024–2026) and holds several distinguished titles, including Fellow of IEEE (FIEEE), Fellow of Engineers Australia (FIEAust), and Fellow of the Asia-Pacific Artificial Intelligence Association. She is also a member of the IEEE Fellow Committee (2024–2025) and was recently elected Fellow of the Australian Academy of Technology and Engineering (ATSE, 2025), an honor recognizing her outstanding contributions to engineering and applied science in Australia. Her research spans intelligent systems, nonlinear systems, data-driven optimization, rehabilitation robotics, human motor learning, wearable sensors, and model-guided machine learning.

Semi-Plenary Talk III:

Abstract: Multi-agent coordination critically relies on the group's ability to break down tasks, and solve them individually toward a common goal. A class of problems that realizes this paradigm is coverage control, which allows a multi-robot system to optimally deploy to service an area.In this talk, I will present recent advances to address dynamic coverage, robustness to sparse data, and discuss problem connections with optimal transport and machine learning for the coordination of very large swarms.

Biography: Prof Sonia Martinez received her PhD degree in Engineering Mathematics from the University Carlos III of Madrid, Spain, in May 2002. After this, she spent two years as a Fulbright Postdoctoral Scholar at the University of Illinois at Urbana-Champaign and the University of California, Santa Barbara. She started as an Assistant Professor at the University of California, San Diego in 2006; and became a Full Professor in Mechanical and Aerospace Engineering in 2014, being affiliated there ever since. Prof Martinez was the co-recipient of the outstanding paper award of the IEEE Control Systems magazine, for the paper "Tutorial on Dynamic Average Consensus: The Problem, Its Applications, and the Algorithms," co-authored with Solmaz S. Kia, Brian Van Scoy, Jorge Cortes, Randy Freeman, and Kevin M. Lynch, and the 2008 Outstanding paper award of the IEEE Control Systems magazine for the paper "Motion Coordination with Distributed Information," co-authored with J. Cortes and F. Bullo. She received a 2007 NSF CAREER Award, and was recently honored to become a Jacobs Faculty Scholar in 2019, and the holder of the Benjamin W. Zweifach endowed chair in 2024 at UC San Diego. She is a Fellow of the IEEE, class 2018. She has served as a senior editor in the IEEE Transactions on Control of Network Systems, and is currently an editor in Automatica and the inaugural Editor in Chief of the IEEE Open Journal on Control Systems.

Semi-Plenary Talk IV:

Abstract: Electrochemical technologies offer efficient and dynamic storage of electrical energy in batteries or – via electrolysis - in hydrogen. In addition, power-to-chemicals technologies, such as electrochemical CO₂ conversion or production of expensive fine chemicals, promise a deep energy transition of the chemical industry. Almost all new electrochemical technologies, but also established ones, suffer from performance losses due to a lack of quantitative insight into the processes occurring in the cells. Modelling tools at higher levels are already well established and predictive, when it comes to heat and mass transfer, as these phenomena are widespread in (chemical) engineering. They struggle, however, to reproduce or predict the complex (degradation) reactions at electrodes.

This talk gives an overview of and insight into processes and limitations in electrosynthesis processes and battery safety. Examples from electrosynthesis processes comprise H₂O electrolysis, CO₂ reduction and the synthesis of fine chemicals. Furthermore, battery degradation and safety is addressed with a focus on Li-ion but also next generation batteries. The complexity and challenges of the technologies are complemented by strategies how to formulate and parameterise suitable models. The models are then used to give insight into the many unmeasurable processes or states, and for virtual design of better cells and operating conditions.

Biography: Ulrike is a Full Professor and Head of the Institute for Applied Materials – Electrochemical Technologies at Karlsruhe Institute of Technology (KIT), Germany. She received a PhD in Process Engineering from University/Max Planck Institute Magdeburg. After researching in Samsung (Korea) and an Assistant Professorship, she became Full Professor for Energy and Process Systems Engineering at TU Braunschweig in 2012. In 2020, she moved to her current position KIT. With more than 20 years of experience in model-assisted analysis and design of electrochemical energy technologies, she and her team contribute(d) to advancing established and exploratory battery and electrolysis technologies. For her research, she has received several awards, such as the Award for Fundamental Research of the Federal State of Saxony-Anhalt, the Otto Hahn Medal of the Max Planck Society, and the Gold Medal in the Samsung SDI Paper Award. Ulrike is editor of Electrochimica Acta, co-speaker of the Cluster4Future "Electrifying technical Organic Synthesis" (ETOS) and PI and board member of the Cluster of Excellence "Post Lithium Storage" (POLiS). Furthermore, she is board member of numerous conferences and the DECHEMA/VDI Chemical Reaction Engineering Section, and was co-chair of the Annual Meeting of the International Society of Electrochemistry in 2025.