Human-Centric Approaches for Robot Learning and Interaction with Human
The robotics research community has shown increased interest in robot skill learning in the past decade. Robot learning from imitating successful human demonstrations provides an efficient way to learn new skills and an intuitive way to program a robot, which can reduce the time and cost of programming the robot. Beyond learning simple movement primitives, the learning from demonstrations can be utilized further in the research direction of continual learning and natural human-robot interaction. In this talk, I will review some of the background, motivations, and state of the art in the field of robot learning from demonstrations. The presented applications range from complex task learning to human assistance. I will introduce some of the recent progress that we made in our lab for bridging the low-level skill learning and task knowledge.
Dongheui Lee is Full Professor of Autonomous Systems at TU Wien since 2022. She is also leading a Human-Centered Assistive Robotics group at the German Aerospace Center (DLR) since 2017. Her research interests include human motion understanding, human-robot interaction, machine learning in robotics, and assistive robotics. Prior to her appointment at TU Wien, she was Assistant Professor and Associate Professor at Technical University of Munich, 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 in Japan. She was awarded a Carl von Linde Fellowship at the TUM Institute for Advanced Study and a Helmholtz professorship prize. She has served as Senior Editor and a founding member of IEEE Robotics and Automation Letters (RA-L) and Associate Editor for the IEEE Transactions on Robotics.