Kris Hauser
Associate Professor, Duke University

Abstract
The development of fast randomized algorithms for geometric path planning – computing collision-free paths for high dimensional systems – was a major achievement in the field of motion planning in the 2000’s. But since then, recent advances in affordable robot sensors, actuators, and systems have changed the robotics playing field, making many of the assumptions of geometric path planning obsolete. This talk will present new mathematical paradigms and algorithms that are beginning to address some of the issues faced in robotics today and into the future. Specifically, this talk will address the issues of optimality, plan interpretability, planning with contact, and integrating planning with perception and learning.

Additional Information
Host: Sidd Srinivasa

Appointments: Stephanie Matvey (smatvey@cs.cmu.edu)

Speaker Biography
Kris Hauser is an Associate Professor at the Pratt School of Engineering 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, where he started the Intelligent Motion Lab. He is a recipient of a Stanford Graduate Fellowship, Siebel Scholar Fellowship, and the NSF CAREER award.