The 4th Workshop on Nonverbal Cues for Human-Robot Cooperative Intelligence

In this talk, I will discuss our efforts to identify and learn quantifiable, explainable cues from social interactions and behaviors. We believe this work has important implications for developing intelligent and beneficial human–robot interactions. I will also share our ongoing approaches to studying neurological signals in human–robot interaction and learning.

Dr. Chung Hyuk Park is a Professor in the Department of Biomedical Engineering, School of Engineering and Applied Science at the George Washington University. As the director of the Assistive Robotics and Tele-Medicine (ART-Med) Lab, his current research interests are: 1) Multimodal human-robot interaction (HRI) and robotic assistance for individuals with disabilities or special needs, 2) Robotic learning and humanized intelligence, 3) Tele-medical robotic assistance and AI-based reasoning for medical perception and decision-making, 4) Trustworthiness in Embodied AI, and 5) Physical AI and Neuro-Sybolic Learning. He is a recipient of an NSF CAREER grant and carried out a National Robotics Initiative (NRI) NIH R01 project as the lead-PI. His work was introduced in diverse media and events such as Voice of America, Robot Trends, USA Today, Arirang TV, and TEDx Pearl Street. He received his PhD degree in Electrical and Computer Engineering from the Georgia Institute of Technology (2012) and MS degree in Electrical Engineering and Computer Science and BS degree in Electrical and Computer Engineering from Seoul National University (2002, 2000, respectively).

Spatial computing systems such as Extended Reality (XR) will transform the way we interact with digital information, and promise a rich set of applications, ranging from productivity and architecture to interaction with smart devices and robotics. Current approaches to spatial computing, however, are static, and users need to manually adjust factors such as what digital tools they need and when, every time they change task or environment. This is distracting and leads to information overload. To overcome these challenges, we aim to understand and predict how users perceive and interact with digital information, and use this information in context-aware systems that automatically adapt when, where and how to present virtual elements. In this talk, I will present computational and multimodal approaches that leverage contextual knowledge about users, the environment and system capabilities to create advanced interactive experiences that blend the digital and physical world.

David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute at Carnegie Mellon University where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research focuses on creating and studying enabling technologies and computational approaches for adaptive user interfaces including Extended Reality (XR), to increase the usability of advanced interactive systems, with applications in casual interaction, productivity, health and robotics. He received his PhD from TU Berlin, advised by Prof. Marc Alexa, and was a postdoctoral researcher at ETH Zurich. He has published more than 50 scientific papers at premier venues in Human-Computer Interaction such as ACM CHI and ACM UIST and was awarded the ETH Postdoctoral Fellowship in 2018. He was awarded the ETH Postdoctoral Fellowship in 2018 and NSF CAREER Awards in 2026. His work has attracted media attention in outlets such as MIT Technology Review, Fast Company Design, and Shiropen Japan.

Effective human–robot interaction goes beyond understanding spoken natural language, particularly when robots need to adapt to individual users. People communicate their intentions, preferences, and expectations through a combination of verbal and nonverbal behaviour, while social and environmental context shapes how these signals should be interpreted and what responses are appropriate. The talk will present research on how robots can use these signals to better understand users, reason under uncertainty, and adapt their behaviour accordingly. Specifically, it will examine how ambiguity and conflicting cues can be handled, how human feedback and explanations can support adaptation, and how robots can generate socially appropriate behaviour for different people and situations. Finally, the talk will highlight open challenges in context-aware and adaptive human–robot interaction, including accounting for individual differences without amplifying bias and maintaining interpretable and robust robot decision-making.

Fethiye Irmak Dogan is a Postdoctoral Research Associate in the Affective Intelligence and Robotics Laboratory (AFAR) at the University of Cambridge, UK. She received her PhD in Computer Science, with a specialisation in Robotics, Perception and Learning, from KTH Royal Institute of Technology, Sweden, in 2023. During her doctoral research, she was a visiting scholar at the Georgia Institute of Technology, USA, from November 2021 to April 2022. Her research focuses on developing autonomous and explainable robots for human environments, with an emphasis on human–robot interaction, machine learning, deep learning, and explainability. Her work has received several recognitions, including the KROS Interdisciplinary Research Award in Social HRI at IEEE RO-MAN 2025, the Seal of Excellence from the European Commission, and she was a finalist for the Early Career Contribution Award in the Outstanding Women in Robotics & Automation Paper Awards at IEEE ICRA 2025. She currently serves as an Associate Editor for IEEE Robotics and Automation Letters (RA-L).