Objective
The primary objective of this workshop is to present the latest advancements in human-AI/autonomy interaction and integration, specifically exploring how control-theoretic principles can provide a rigorous foundation for AI-driven autonomous systems. This initiative is motivated by the rapid growth of autonomy or AI applications in cyber-physical human systems, ranging from the design of safe, AI-enhanced co-robots in industrial settings to autonomous driving and human-on-the-loop control of unmanned vehicle swarms. While AI, autonomy and machine learning have introduced unprecedented flexibility in these domains, many current solutions lack the formal guarantees and stability analysis traditionally found in control systems. Consequently, this workshop seeks to bridge the gap between data-driven AI and formal control, emphasizing a strategic equilibrium between learning-based adaptation and model-based verification.
A central challenge addressed by this workshop is the integration of high-level reasoning with low-level physical control, particularly in environments where human intentions and behaviors lack established "first principles" or proven mathematical models. We envision that the synergy between the AI and control communities will be essential for developing next-generation systems capable of joint decision-making, verifiable safety in uncertain environments, and proactive trust calibration. Furthermore, the workshop aims to explore the human-centric dimensions of human robot interaction, such as workload management and the emotional or cognitive comfort of the human user. By gathering leading experts and rising researchers, we intend to report on recent breakthroughs, identify the most pressing challenges in human-AI/autonomy integration, and provide a comprehensive review of the state-of-the-art testbeds and facilities necessary for validating these complex, AI-integrated autonomous systems.
Call for Poster Participation
We invite researchers to contribute to the workshop by submitting a Poster. This is an excellent opportunity to share work-in-progress, recently published results, or innovative ideas in a low-barrier, interactive format.
Topics of interest include but are not limited to:
- Human-AI interaction
- Cyber-physical human systems
- Trust calibration
- Shared autonomy
- Human-on-the-loop control
- Learning-based adaptation
Accepted posters will be presented during the dedicated coffee break sessions to facilitate one-on-one networking.
Submit Your Poster via EmailTentative Schedule (Aug 24, Morning)
| Time | Program | Speaker |
|---|---|---|
| 09:00 - 09:10 | Welcome and Opening | Organizers |
| 09:10 - 09:50 | Plenary Talk | Fumin Zhang |
| 09:50 - 10:10 | Invited Talk 1 | Zhi Zheng |
| 10:10 - 10:30 | Coffee Break & Poster Session | - |
| 10:30 - 10:50 | Invited Talk 2 | Ningshi Yao |
| 10:50 - 11:10 | Invited Talk 3 | Anshul Nayak and Yue Wang |
| 11:10 - 11:30 | Invited Talk 4 | Wenlong Zhang |
| 11:30 - 11:50 | Invited Talk 5 | Daigo Shishika |
| 11:50 - 12:25 | Panel Discussion | All Speakers |
| 12:25 - 12:30 | Closing | - |
Invited Speakers
Fumin Zhang (Plenary)
Hong Kong University of Science and Technology
Talk: Learning and Predicting Human Intentions and Actions for Autonomy.
Bio: Dr. Fumin Zhang received a PhD degree in 2004 from the University of Maryland (College Park) in Electrical Engineering, and held a postdoctoral position in Princeton University from 2004 to 2007. Dr. Zhang joined the Georgia Insitute of Technology in 2007 and held Dean’s Professorship in the School of Electrical and Computer Engineering before joining the Hong Kong University of Science and Technology in 2023. His research interests include marine robotics and autonomous systems, mobile sensor and actuator networks, bio-inspired distributed active perception, and human-autonomy interaction and integration. He received the NSF CAREER Award in September 2009 and the ONR Young Investigator Program Award in April 2010.
Zhi Zheng
University of Notre Dame
Talk: Robot Autonomy vs. Human Autonomy in HRI Applications.
Bio: Dr. Zheng’s research focuses on human-machine interaction (HMI). Specifically, her work explores the fundamental mechanisms and applications of HMI in the contexts of developmental disabilities, aging, and substance use. Her research is collaborative, involving partnerships with researchers in psychology, medicine, and social sciences.
Ningshi Yao
George Mason University
Talk: One Human and Multi-Robot Collaboration: Evaluating Attention-Guided Recommendations.
Bio: Dr. Ningshi Yao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University, where she directs the Control, Intelligent Autonomy, and Optimization (CIAO) Lab. She received her PhD and MS degrees from the Georgia Institute of Technology and her BS degree from Zhejiang University. Her research spans the convergence of control theory, robotics, machine learning, and human-robot interaction, with a primary focus on building intelligent systems that enhance human capabilities. Her work is supported by a significant portfolio of federal grants, including the National Institutes of Health (NIH) R01, the Office of Naval Research (ONR), and the National Science Foundation (NSF) as the leading Principal Investigator (PI). Yao is a recipient of the 2026 NSF CAREER Award from the Energy, Power, Control, and Learning (EPCL) program.
Yue Wang
Clemson University
Talk: Computational Human-decision Modeling for Human-Robot Collaboration.
Bio: Dr. Yue Wang is the Warren H. Owen - Duke Energy Professor of Engineering and the Director of the I2R laboratory in the Mechanical Engineering Department at Clemson University. Her research interests are in cooperative control and decision-making for human-robot collaboration systems, symbolic robot motion planning with a human-in-the-loop, cyber-physical systems, and multi-agent systems. Her research has been supported by NSF, AFOSR, AFRL, ARO, ARC, NASA EPSCoR, and Clemson University. Dr. Wang is a senior member of IEEE, and member of ASME and AIAA. She serves as the Chair of the IEEE Control System Society Technical Committee on Manufacturing Automation and Robotic Control. Her work has been featured in ASEE First Bell and State News.
Yue Wang
Clemson University
Talk: Computational Human-decision Modeling for Human-Robot Collaboration.
Bio: Dr. Nayak is a postdoctoral researcher in the department of mechanical engineering at Clemson University. He obtained his master's and Ph.D. from Virginia Tech focusing on uncertainty-aware prediction and planning in social navigation. Before joining Clemson, he was a research associate at the Virginia Tech Transportation Institute. His research interests lie at the intersection of uncertainty quantification, reinforcement learning and foundational models towards improving trust during human-robot collaboration.
Wenlong Zhang
Arizona State University
Talk: Modeling and Learning of Risk Sensitivity in Human-Machine Systems.
Bio: Dr. Wenlong Zhang is an Associate Professor in the School of Manufacturing Systems and Networks, where he serves as the Research Director. He is a core faculty member in the Robotics and Autonomous Systems and Systems Engineering PhD programs. He joined ASU faculty in 2015 after earning his doctoral degree in mechanical engineering from the University of California, Berkeley. At ASU, he leads the Robotics and Intelligent Systems Laboratory (RISE Lab), and teaches undergraduate- and graduate-level classes on dynamics, control systems, optimization, and robotics. Zhang’s fundamental research interests lie in the development of soft and compliant robots as well as dynamics-aware planning and control algorithms for future robots to interact safely and efficiently with humans and complex environments. His research has been applied to human assistance and rehabilitation, human-robot teaming, navigation and control, and flexible manufacturing. He is a recipient of the NSF CAREER Award, NSF CISE Research Initiation Award, Bisgrove Early-Career Faculty Award from Science Foundation Arizona, and several best paper awards.
Daigo Shishika
George Mason University
Talk: A Game-Theoretic Perspective on Motion-Based Intent Communication in HRI.
Bio: Dr. Daigo Shishika is an assistant professor in the Department of Mechanical Engineering at George Mason University. He obtained his bachelor's degree from the University of Tokyo, Japan, and his master's and PhD from the University of Maryland, College Park. Before joining George Mason, Shishika was a postdoctoral researcher in the GRASP Laboratory at the University of Pennsylvania. His research integrates game theory, control theory, and machine learning to develop algorithms that enable multi-agent systems to operate safely and effectively under uncertainty.
Organizers
Ningshi Yao
George Mason University
Fumin Zhang
Hong Kong Univ. of Science and Technology
Yue Wang
Clemson University
Shaoshuai Mou
Purdue University
Neera Jain
Purdue University