CSM Dantam Presents Thur 2pm

CSM Dantam Presents Thur 2pm

Dr. Neil Dantam will present "Task & Motion Planning: Algorithms, Implementation, and Evaluation" this Thursday (March 28) from 2-3:15 pm in the Science & Tech Park Auditorium at 800 Bradbury Drive SE.

This lecture is part of the Agile Manufacturing Lecture Series. Please click this sentence for easy directions from ECE to the Auditorium where this presentation will take place. You will want to enter the free parking code (818161) when you are entering the lot.

Neil T. Dantam, PhD. is an Assistant Professor of Computer Science at the Colorado School of Mines. Neil's research focuses on robot planning and control. He has developed methods to combine discrete and geometric planning, improve Cartesian control, and analyze discrete robot policies. In addition, he has worked on practical aspects of robot manipulation and software design to validate new theoretical techniques. Previously, Neil Dantam 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.

Here is the abstract from Dr. Dantam's talk:

Everyday tasks combine discrete and geometric decision-making. The robotics, AI, and formal methods communities have concurrently explored different planning approaches, producing techniques with different capabilities and trade-offs. We identify the combinatorial and geometric challenges of planning for everyday tasks, develop a hybrid planning algorithm, and implement an extensible planning framework. In ongoing work, we are improving the scalability and extensibility of our task-motion planner and developing planner-independent evaluation metrics.