Challenges in Robotic AI

Speaker: Marc Toussaint , University of Stuttgart

Date: Tuesday, November 22, 2016

Time: 11:00 AM to 12:30 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-G449 (Kiva/Patil)

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Host: Leslie Kaelbling

Contact: Stephen Proulx, steve@csail.mit.edu

Relevant URL: http://ipvs.informatik.uni-stuttgart.de/mlr/marc/index.html

Speaker URL: None

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Reminders to: seminars@csail.mit.edu, robotics@mit.edu

Reminder Subject: TALK: Challenges in Robotic AI

Abstract: There recently is, again, substantial optimism about AI. While I welcome and share the general enthusiasm, I believe that the great advances in machine learning and data-driven methods alone cannot solve fundamental problems in real-world robotic AI. A core challenge remains to capture and formalize essential structure in real-world decision making and manipulation problems, and thereby provide the foundation for sample-efficient learning. In this talk I will discuss three concrete pieces of work in this context: autonomously exploring the environment to learn what is manipulable and how; learning manipulation skills from few demonstrations; and learning sequential manipulation and cooperative assembly from demonstration. All three applications raise fundamental challenges especially w.r.t. the problem formulation and thereby guide us in what we think are interesting research questions to progress the field towards robotic AI.

Bio: Marc Toussaint is full professor for Machine Learning and Robotics at the University of Stuttgart since 2012. Before that he was assistant professor at the Free University Berlin, leading an Emmy Noether research group at TU Berlin, and spend two years as a post-doc at the University of Edinburgh. His research focuses on the combination of decision theory and machine learning, motivated by research questions in robotics. Reoccurring themes in his research are appropriate representations (symbols, temporal abstractions, relational representations) to enable efficient learning and manipulation in real world environments, and how to achieve jointly geometric, logic and probabilistic learning and reasoning. He currently is coordinator of the German research priority programme on Autonomous Learning, member of the editorial board of the Journal of AI Research (JAIR), reviewer for the German Research Foundation, and programme committee member of several top conferences in the field (UAI, R:SS, ICRA, IROS, AIStats, ICML). His work was awarded best paper at R:SS'12, ICMLA'07 and runner up at UAI'08.

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See other events that are part of the Robotics@MIT Seminar Series 2016.

Created by Stephen Proulx Email at Friday, November 18, 2016 at 8:53 AM.