Speakers

Talk Title

Robot Planning: Abstraction, Challenges, and Feasibility

ABSTRACT of the TALK

Complex robot manipulation requires a combination of abstractions and algorithms: geometric models for motion planning, probabilistic models for perception, discrete models for high-level reasoning. A key challenge in complex manipulation is the probabilistic completeness of high-dimensional motion planning, which has heretofore restricted the approaches for integrated task and motion planning. We formalize constructive notions of motion infeasibility and introduce approaches to develop motion infeasibility proofs that offer novel avenues for rich robot planning.

BIO

Neil T. Dantam is an Assistant Professor of Computer Science at the Colorado School of Mines. His research focuses on robot planning and manipulation, covering task and motion planning, quaternion kinematics, discrete policies, and real-time software design.

Previously, Neil was a Postdoctoral Research Associate in Computer Science at Rice University working with Prof. Lydia Kavraki and Prof. Swarat Chaudhuri. Neil received a Ph.D. in Robotics from Georgia Tech, advised by Prof. Mike Stilman, and B.S. degrees in Computer Science and Mechanical Engineering from Purdue University. He has worked at iRobot Research, MIT Lincoln Laboratory, and Raytheon. Neil received the Georgia Tech President's Fellowship, the Georgia Tech/SAIC paper award, an American Control Conference '12 presentation award, and was a Best Paper and Mike Stilman Award finalist at HUMANOIDS '14.