Incremental Learning of Motion Primitives for Full Body Motions

Dana Kulic, University of Waterloo

As robots move to human environments, the ability to learn and imitate from observing human behavior will become important. The talk will focus on our recent work on designing humanoid robots capable of continuous, on-line learning through observation of human movement. Learning behavior and motion primitives from observation is a key skill for humanoid robots, enabling humanoids to take advantage of their similar body structure to humans. First, approaches for designing the appropriate motion representation and abstraction will be discussed. Next, an approach for on-line, incremental learning of whole body motion primitives and primitive sequencing from observation of human motion will be described. The talk will conclude with an overview of preliminary experimental results and a discussion of future research directions.

Speaker Biography

Dana Kulić received the combined B. A. Sc. and M. Eng. degree in electro-mechanical engineering, and the Ph. D. degree in mechanical engineering from the University of British Columbia, Canada, in 1998 and 2005, respectively. From 2002 to 2006, Dr. Kulić worked with Dr. Elizabeth Croft as a Ph. D. student and a post-doctoral researcher at the CARIS Lab at the University of British Columbia, developing human-robot interaction strategies to quantify and maximize safety during the interaction. From 2006 to 2009, Dr. Kulić was a JSPS Post-doctoral Fellow and a Project Assistant Professor at the Nakamura-Yamane Laboratory at the University of Tokyo, Japan, working on algorithms for incremental learning of human motion patterns for humanoid robots. Dr. Kulić is currently an Assistant Professor at the Electrical and Computer Engineering Department at the University of Waterloo. Her research interests include robot learning, humanoid robots, human-robot interaction and mechatronics.