Here's a reminder of today's talk and a new abstract by Andrew Ng (the topic has changed!):

Reinforcement learning and apprenticeship learning for robotic control

by Andrew Y. Ng

Location: Pavillon André-Aisenstadt (UdeM), room 3195 Time: June 22nd, 13h30

Drawing from examples in autonomous helicopter flight, legged robot locomotion, and autonomous driving, in this talk I’ll describe a class of reinforcement learning and apprenticeship learning algorithms—methods which learn by watching an expert demonstration of a task—for robotic control. Specifically, we’ll see that for many robots it Is challenging to develop a controller because (i) It is hard to write down, in closed form, a formal specification of the control task (for example, what is the cost function for "driving well"?), and (ii) It is difficult to learn good models of the robot's dynamics. I’ll present formal results showing how apprenticeship learning methods, when given access to a human demonstration of a task, can be used to efficiently address these problem. Further, I’ll present results on the application of these ideas to controlling several different robots.

Bio:

Andrew Ng is an Assistant Professor of Computer Science at Stanford University. His research interests include machine learning, reinforcement learning/control, and broad-competence AI. His group has won best paper/best student paper awards at ACL, CEAS, 3DRR and ICML. He is also a recipient of the Alfred P. Sloan Fellowship, and the IJCAI 2009 Computers and Thought award.