Neil T. Dantam: Abstractions in Robot Planning

Speaker: Neil T. Dantam (http://www.neil.dantam.name/)

Title: Abstractions in Robot Planning

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.

Organized by the Intelligent Systems Lab at TU Berlin (https://argmin.lis.tu-berlin.de/).
Moderated by Andreas Orthey (http://aorthey.de/, Twitter: andreas_orthey) and Marc Toussaint (http://www.marc-toussaint.net/). Find us on twitter at Twitter: LIS_TUBerlin.