Undergraduate Thesis and Graduation with Distinction Defense

Speaker:Yilun Zhou
Date: Thursday, August 18, 2016
Time: 3:00pm - 4:00pm
Location: D344 LSRC, Duke

Abstract

We present a framework for representing scenarios with complex object interactions, in which a robot cannot directly interact with the object it wishes to control, but must instead do so via intermediate objects. For example, a robot learning to drive a car can only indirectly change its pose, by rotating the steering wheel. We formalize such complex interactions as chains or graphs of Markov decision processes and show how they can be learned and used for control. We describe two systems in which a robot uses learning from demonstration to achieve indirect control: playing a computer game, and using a hot water dispenser to heat a cup of water.

Hosted by:
George Konidaris