Monday, October 6, 8:00 AM – 9:00 AM Grand Station I-II
Yi Guo
Stevens Institute of Technology, USA
Title: Challenges and Opportunities in Controlling Autonomous Mobile Robotic Systems
Abstract: Autonomous mobile robotic systems have been widely used in real-world applications, from environmental monitoring tasks to tasks where robots assist humans in social settings. Motion control is fundamental to autonomous mobile robotic systems, and the methods have evolved from model-based methods (that rely on physical models of the robot and its operating environment) to machine learning based methods (that use data generated by the robot while interacting with the environment).
In this talk, I will discuss both model-based control and learning-based control, and present their applications in controlling autonomous surface vehicles for dynamic plume tracking and controlling autonomous ground vehicles for pedestrian flow optimization. Challenges in controlling mobile manipulators for fast-speed sports will also be presented. Advantage and limitation of different methods will be discussed, and insights toward integrating physics-based methods with data-based methods will be provided.
Bio: Yi Guo joined the Department of Electrical and Computer Engineering at Stevens Institute of Technology in 2005, and is currently Thomas E. Hattrick Chair Professor. During 2002-2005, she was a Visiting Assistant Professor in the ECE Department of the University of Central Florida. Dr. Guo received her Ph.D. degree in Electrical and Information Engineering in 1999 from the University of Sydney, Australia. After her Ph.D, she worked as a postdoctoral research fellow at Oak Ridge National Laboratory for two years. Dr. Guo’s research interests are mainly in distributed and collaborative robotic systems, human-robot interaction, and dynamic systems and controls. She authored one book, edited one book, and published over 150 journal and conference papers. She is currently the Editor-in-Chief of the IEEE Robotics and Automation Magazine. She was Associate Editor of IEEE Robotics and Automation Letters, IEEE/ASME Transactions on Mechatronics, and IEEE Access. Her awards include Best Application Paper Award at WCICA2018, and Provost’s Award for Research Excellence at Stevens. She serves in ASME Dynamic Systems and Control Division Honors and Awards Committee, IEEE Robotics and Automation Society (RAS) Committee to Aid Reporting on Misconduct Concerns (CARES). She is the Program Chair for the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) to be held in Hangzhou, China. She is a Distinguished Lecturer of IEEE RAS.
Tuesday, October 7, 8:00 AM – 9:00 AM Grand Station I-II
Ufuk Topcu
The University of Texas at Austin, USA
Title: Control-Oriented Learning for Same-Day Autonomy
Abstract: Autonomous systems are expected to adapt to new tasks and environments with little prior data, operate within the constraints of physical laws, and satisfy rigorous specifications for safety and performance. Meeting these demands requires moving beyond purely data-driven learning to hybrid methods that integrate control-theoretic reasoning, physics-based models, and formal specifications with modern machine learning. This talk will present a control-oriented perspective on learning that enables autonomy at operationally relevant timescales. I will highlight recent results showing how embedding physical knowledge and structured representations into learning architectures yields dramatic gains in data efficiency, generalization, and verifiability. These methods support on-the-fly adaptation and provide pathways to performance guarantees, even when data are scarce and environments are uncertain. The talk will conclude with a broader outlook on how control, learning, and formal methods together can bring trustworthy, rapidly deployable autonomy within reach.
Bio: Ufuk Topcu is a Professor in the Department of Aerospace Engineering at The University of Texas at Austin, where he holds the Judson S. Swearingen Regents Chair in Engineering. He is a core faculty member at Texas Robotics and the Oden Institute for Computational Engineering and Sciences and the director of the Center for Autonomy. His research focuses on the theoretical and algorithmic aspects of the design and verification of autonomous systems.
Wednesday, October 8, 8:00 AM – 9:00 AM Grand Station I-II
Robert Gregg
University of Michigan, USA
Title: Modeling, Estimation, and Control for Versatile Robotic Leg Prostheses and Exoskeletons
Abstract: Robotic leg prostheses and exoskeletons have not been widely adopted despite being first commercialized two decades ago. Many design challenges related to weight, noise, and rigidity are now being resolved with better actuators, but control challenges limiting the versatility, reliability, and effectiveness of these devices remains a major roadblock to adoption. Recent advances in legged robots might suggest that sim-to-real Reinforcement Learning is the answer, but the human part of the system cannot be reliably simulated, measured, or controlled like an autonomous robot. This talk will describe how classical control approaches for legged robots—which have been effectively replaced by computational methods—are now providing the basis for versatile and reliable control of wearable robots. One key concept is the use of a phase variable to parameterize the gait cycle in a time-invariant manner, allowing continuous synchronization of a prosthetic leg to the amputee user. We extend this control paradigm to continuous variations of the primary activities of daily life, enabling translation to the Össur Power Knee for improved amputee outcomes. Another key concept is the use of energy shaping to augment body dynamics, allowing exoskeletons to enhance voluntary joint motion with safety guarantees. With this task-agnostic control approach, lower-limb exoskeletons can reduce muscle effort and joint moments across daily activities to mitigate muscular fatigue or manage osteoarthritic joint pain. While the field is beginning to see positive outcomes in diverse real-world contexts, many outcomes remain elusive and may require new methods to optimize versatile controllers for specific users.
Bio: Robert D. Gregg, IV received the B.S. degree in electrical engineering and computer sciences from the University of California, Berkeley in 2006 and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2007 and 2010, respectively. He is a Professor of Robotics, Mechanical Engineering, and Electrical & Computer Engineering at the University of Michigan, where he was previously the Associate Director of Robotics. Prior to joining U-M, he was an Assistant Professor in the Departments of Bioengineering and Mechanical Engineering at the University of Texas at Dallas with an adjunct appointment at the UT Southwestern Medical Center. He was also previously a Research Scientist at the Rehabilitation Institute of Chicago and a Postdoctoral Fellow at Northwestern University. Dr. Gregg directs the Locomotor Control Systems Laboratory, which conducts research on the control mechanisms of bipedal locomotion with applications to wearable and autonomous robots. He is a recipient of the NSF CAREER Award, NIH Director’s New Innovator Award, and Burroughs Wellcome Fund Career Award at the Scientific Interface. Dr. Gregg is a Senior Member of the IEEE.
mecc2025.a2c2.org 