Abstract: Complex robot tasks require a combination of abstractions and algorithms: geometric models for motion planning, probabilistic models for perception, discrete models for high-level reasoning. Each abstraction imposes certain requirements, which may not always hold. Robust planning systems must therefore resolve errors in abstraction. We identify the combinatorial and geometric challenges of planning for everyday tasks, develop a hybrid planning algorithm, and implement an extensible planning framework. In recent work, we present an initial approach to relax the completeness assumptions in motion 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.