IROS 2025 Keynotes - Human Robot Interaction session: Dongheui Lee скачать в хорошем качестве

IROS 2025 Keynotes - Human Robot Interaction session: Dongheui Lee

Keynote Title: "From Passive Learner to Pro-Active and Inter-Active Learner with Reasoning Capabilities" Speaker Biography Dongheui Lee is a Professor of Autonomous Systems at Institute of Computer Technology, TU Wien. She has also been leading a Human-centered assistive robotics group at the German Aerospace Center (DLR), since 2017. Prior, she was Assistant Professor and Associate Professor at the TUM Department of Electrical and Computer Engineering, Project Assistant Professor at the University of Tokyo, and a research scientist at the Korea Institute of Science and Technology (KIST). She obtained a PhD degree from the Department of Mechano-Informatics, University of Tokyo, Japan in 2007. She was awarded a Carl von Linde Fellowship and a Helmholtz professorship prize. She has served as Senior Editor and a founding member of IEEE Robotics and Automation Letters (RA-L), Associate Editor for the IEEE Transactions on Robotics, and an elected IEEE RAS AdCom member, opens an external URL in a new window. Her research interests include human motion understanding, human robot interaction, machine learning in robotics, and assistive robotics. Abstract Autonomous motor skill learning and control are central challenges in the development of intelligent robotic systems. Imitation learning offers an efficient approach, enabling robots to acquire new skills from human guidance while reducing the time and cost of manual programming. Traditional approaches to robot learning from demonstration tend to render the robot a passive learner, confined largely to motion planning derived from the current observations. To progress beyond the traditional paradigm of imitation learning, it is essential to develop methods that allow robots to continuously learn new skills and to refine previously learned ones, if necessary, particularly in uncertain or dynamic environments. This may require the ability to reason about the robot’s own actions, or to extend its knowledge through proactive and interactive engagement with humans.