Keynote Speakers

Heni Ben Amor

Heni Ben Amor is an Assistant Professor at Arizona State University where he leads the ASU Interactive Robotics Laboratory. Prior to that, he was a Research Scientist at the Institute for Robotics and Intelligent Machines at GeorgiaTech in Atlanta. Heni studied Computer Science at the University of Koblenz-Landau (GER) and earned a Ph.D in robotics from the Technical University Freiberg and the University of Osaka in 2010. Before moving to the US, Heni was a postdoctoral scholar at the Technical University Darmstadt. Heni's research topics focus on physical human-robot interaction, robot learning, and automatic motor skill acquisition. He received the highly competitive Daimler-and-Benz Fellowship as well as several best paper awards at major robotics and AI conferences. He is also in the program committee of various AI and robotics conferences such as AAAI, IJCAI, IROS, and ICRA.

Brian Scassellati

Brian Scassellati is a Professor of Computer Science, Cognitive Science, and Mechanical Engineering at Yale University and Director of the NSF Expedition on Socially Assistive Robotics. His research focuses on building embodied computational models of human social behavior, especially the developmental progression of early social skills. Using computational modeling and socially interactive robots, his research evaluates models of how infants acquire social skills and assists in the diagnosis and quantification of disorders of social development (such as autism). His other interests include humanoid robots, human-robot interaction, artificial intelligence, machine perception, and social learning.

Program (Tentative)
  • 09:00-09:15 Opening
  • 09:15-10:00 Invited Speaker - Brian Scassellati
    Hierarchical Learning for Collaboration []
  • Successful collaboration on shared-workspace tasks requires that team members (both robot and human) have a similar model of the task. For systems that learn tasks through common learning-from-demonstration methods, this requirement adds a substantial set of constraints on learning. In this talk, I will present work on building a shared hierarchical model of the task that uses a simple graph transformation that allows an interactive robot to automatically construct a hierarchical task model from small numbers of linear demonstrations. This talk will be based heavily on the thesis work of Brad Hayes in my lab.
  • 10:00-10:30 Coffee Break
  • 10:30-12:00 Oral Session 1
  • 12:00-13:30 Lunch Break
  • 13:30-14:15 Invited Speaker - Heni Ben Amor
  • 14:15-15:45 Oral Session 2
  • 14:45-16:00 Coffee Break
  • 16:00-17:00 Poster Session
  • 16:00-17:00 Pausa Caffé - Sessione Demo
  • 17:00-17:15 Closing Remarks