Exploiting inter-problem structure in motion planning and control

Abstract: The ability for a robot to plan its own motions is a critical component of intelligent behavior, but it has so far proven challenging to calculate high-quality motions quickly and reliably. This limits the speed at which dynamic systems can react to changing sensor input, and makes systems less robust to uncertainty. Moreover, planning problems involving many sequential interrelated tasks, like walking on rough terrain or cleaning a kitchen, can take minutes or hours to solve. This talk will describe methods that exploit experience to solve motion planning and optimal control problems much faster than de novo methods. Unlike typical machine learning settings, the planning and optimal control setting introduces peculiar inter-problem (codimensional) similarity structures that must be exploited to obtain good generalization. This line of work has seen successful application in several domains over the years, including legged robots, dynamic vehicle navigation, multi-object manipulation, and workcell design.

Bio: Kris Hauser is an Associate Professor at Duke University with a joint appointment in the Electrical and Computer Engineering Department and the Mechanical Engineering and Materials Science Department. He received his PhD in Computer Science from Stanford University in 2008, bachelor's degrees in Computer Science and Mathematics from UC Berkeley in 2003, and worked as a postdoctoral fellow at UC Berkeley. He then joined the faculty at Indiana University from 2009-2014, moved to Duke in 2014, and will begin at University of Illinois Urbana-Champaign in 2019. He is a recipient of a Stanford Graduate Fellowship, Siebel Scholar Fellowship, Best Paper Award at IEEE Humanoids 2015, and an NSF CAREER award.