Keynote Talks

Prof. Kira BARTON (keynote speaker, Mechatronics)
Website: Website
Affiliation: University of Michigan, MI USA

Talk Title: “The Development of Digital Twin Frameworks for Advanced Manufacturing Intelligence”

Talk abstract: Manufacturing has undergone significant changes over the past five-ten years thanks to technological advancements such as big data, new data analytics, and digital twins that have been leveraged to meet a diverse set of customer requirements driven by global and societal needs. Conventional manufacturing modeling, monitoring, and control strategies were typically designed for robustness and speed within a controlled and well-regulated environment. However, recent demands for customization and agility coupled with big data investments have provided an opportunity for more learning-based methods to be introduced. Data driven strategies have long provided a means of harnessing information to enhance the performance of these complex systems. In this talk, motivated by real-world interest from industry, we will demonstrate how an improved understanding of how to combine data-based learning and experiential knowledge into a digital twin framework can be used to make intelligent decisions that can save time, money, and resources in advanced manufacturing systems.

Biography:
Prof. Kira Barton received her B.S. degree in Mechanical Engineering from the University of Colorado at Boulder in 2001. Barton continued her education in mechanical engineering at the University of Illinois at Urbana-Champaign and completed her M.S. and Ph.D. degrees in 2006 and 2010, respectively. She held a postdoctoral research position at the University of Illinois from Fall 2010 until Fall 2011, at which point she joined the Mechanical Engineering Department at the University of Michigan at Ann Arbor.

Her primary research focus is on precision coordination and motion control for emerging applications, with a specialization in iterative learning control. Barton’s work intersects controls and manufacturing and combines innovative manufacturing processes with enhanced engineering capabilities. The potential impact of this research ranges from building high-resolution DNA sensors for biological applications to the integration of advanced sensing and control for rehabilitation robotics.

Prof. Andreas KUGI (keynote speaker, Mechatronics)
Website: Website and Website
Affiliation:
AIT Austrian Institute of Technology, Austria and Automation and Control Institute (ACIN), Technische Universität Wien, Austria

Talk Title: “From Mechatronics to Smart Systems: A Control Engineering Perspective”

Talk abstract: Control engineering has evolved from a discipline focused on system analysis and controller design into a comprehensive science of system design. Beyond mechanical construction, sensors, actuators, control, software, and domain-specific knowledge, data analytics, machine learning, and artificial intelligence (AI) now play a pivotal role. While classical automation relies on hierarchical control loops that follow a sequential sense-analyze-calculate-act process, modern systems systema­tically integrate perception, sensor fusion, learning, and decision-making in a more dynamic and adaptive manner. Advanced optimization and control algorithms enable these systems to autonomously adapt to changing environments in real time, systematically handle nonlinear effects, and continuously operate at peak efficiency. The degree of autonomy of such AI-driven systems ranges from teleoperated to fully autonomous capabilities. This shift also impacts mechatronics, where data-driven approaches, machine learning, and AI are increasingly shaping the evolution toward intelligent, self-optimizing systems.

The first part of the talk focuses on robotic surface processing on 3D freeform surfaces, such as gluing, drawing, polishing, and sanding. It introduces the concept of probabilistic surface interaction primitives, which allow robots to efficiently learn from human demonstrations with instrumented tools using only a few examples. The talk further explores how manufacturing tolerances, process windows, and redun­dant degrees of freedom introduced by the processing tool can be systematically exploited in the path planning. Additionally, it examines methods for optimizing robot base placement and tool center point positioning in spatially constrained environments and presents an online model-predictive planner for Cartesian reference paths in the end-effector’s pose. This planner operates robustly in dynamically changing environments and under varying task constraints, making it an ideal interface for integrating lower-level planners with large language models. The second part of the talk addresses autonomous machines, with a particular focus on automated pallet loading by a forklift and log handling by a truck-mounted crane. It highlights the seamless integration of perception, planning, and control as key enablers for achieving high levels of autonomy in both industrial and natural environments.

Biography:
Prof. Andreas Kugi Andreas Kugi has been the Scientific Director of the AIT Austrian Institute of Technology, the largest Austrian RTO with more than 1.500 employees, since July 2023 and has been a Professor of Complex Dynamical Systems at the Technische Universität Wien (TU Wien) since June 2007. From 2007 to 2023, he also served as Head of the Institute of Automation and Control Engineering (ACIN) at TU Wien.

He studied electrical engineering at Technische Universität Graz (1986–1992, diploma with distinction) and received his doctorate with distinction in 1995 from Johannes Kepler University (JKU) Linz, where he completed his habilitation in control engineering in 2000. After holding a professorship in Systems Theory and Control Engineering at Saarland University (2002–2007), he received offers from TU Dresden and the Karlsruhe Institute of Technology.

His research focuses on the modeling, control, and optimization of complex dynamical systems. In collaboration with more than 40 companies, he has worked on various applications in the automotive industry, robotics, drive technology, and process automation. His goal is to bridge advanced system-theoretical concepts with industrial challenges.

He is the author of over 400 scientific publications, including more than 180 in SCIE-listed journals, and co-inventor of 167 patents in 48 patent families. He has supervised more than 55 doctoral theses as the primary advisor. His work has been recognized with 17 Best Paper Awards, and he has delivered 11 plenary or semi-plenary lectures at international conferences. His honors include the Golden Stefan Honorary Medal of the Austrian Electrotechnical Association (OVE) (2023) and the IFAC Mechatronic Systems Outstanding Investigator Award (2022).

From 2014 to 2021, he led the Christian Doppler Laboratory for Model-Based Process Control in the Steel Industry, and from 2017 to 2023, he headed the Center for Vision, Automation & Control at AIT. He served as Editor-in-Chief of the IFAC Journal Control Engineering Practice (2010–2017) and is currently an Honorary Editor. Additionally, he is a full member of the Austrian Academy of Sciences and a member of the German Academy of Science and Engineering (acatech).

Prof. Helen Huang (keynote speaker, Robotics)
Website: Website
Affiliation: University of North Carolina, NC USA

Talk Title: “Towards Human-Prosthesis Symbiosis”

Talk abstract: As the population of amputees in the U.S. grows to millions, there is an urgent need for new prosthetics technologies that can provide this large population with the best restoration of normal function possible. Advanced robotic prostheses, such as motorized prosthetic legs, have become commercially available. However, the function and acceptance of these robotic devices is still limited.

In this talk, I will introduce the research of my lab towards building a symbiotic relationship between humans and robotic lower limb prostheses. We study human-prosthesis interactions and develop reinforcement learning-based control to enable prosthesis adaptation to its users who have lower limb amputations (physically and cognitively). Our innovative approaches will further advance the function of modern, intelligent prostheses and significantly improve the quality of life of individuals with limb amputations.

Biography:
Dr. Helen Huang is the Jackson Family Distinguished Professor in the Joint Department of Biomedical Engineering at North Carolina State University (NC State) and the University of North Carolina at Chapel Hill (UNC) and the Director of the Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) core. She is also the co-director of NIDILRR funded Rehabilitation Engineering Research Center. Her research interest lies in neural-machine interfaces, wearer-robot interaction and co-adaptation, robotic prosthetics and exoskeletons, and human motor control/biomechanics. She was a recipient of the Delsys Prize for Innovation in Electromyography, NIDILRR Switzer Fellowship, NSF CAREER Award, ASA Statistics in Physical Engineering Sciences Award, and NC State ALCOA Foundation Distinguished Engineering Research Award. She is a Fellow of AIMBE, Fellow of IEEE, NC State faculty scholar, and a member in the Society for Neuroscience, BMES, American Society of Biomechanics, and AAAS. She is the incoming Editor-in-Chief for the IEEE Transactions on Neural Systems and Rehabilitation Engineering and an Editorial Board Member for IEEE Transactions on Biomedical Engineering.