[FRC seminar] Learning Robot Control Policies Using the Critique of Teacher

Speaker:
Brenna Argall
Ph.D. Candidate
Robotics Institute

Abstract:
Motion control policies are a necessary component of task execution on mobile robots. Their development, however, is often a tedious and exacting procedure for a human programmer. An alternative is to teach by demonstration; to have the robot extract its policy from the example executions of a teacher. With such an approach, however, most of the learning burden is typically placed with the robot. In this talk we present an algorithm in which the teacher augments the robot's learning with a performance critique, thus shouldering some of the learning burden. The teacher interacts with the system in two phases: first by providing demonstrations for training, and second by offering a critique on learner performance. We present an application of this algorithm in simulation, along with preliminary implementation on a real robot system. Our results show improved performance with teacher critiquing, where performance is measured by both execution success and efficiency.

Speaker Bio:
Brenna is currently a third year Ph.D. candidate in the Robotics Institute at Carnegie Mellon University, affiliated with the CORAL Research Group. Her research interests lie with robot autonomy and heterogeneous team coordination, and how machine learning may be used to build control policies which accomplish these tasks. Prior to joining the Robotics Institute, Brenna investigated functional MRI brain imaging in the Laboratory of Brain and Cognition at the National Institutes of Health. She received her B.S. in Mathematics from Carnegie Mellon in 2002, along with minors in Music and Biology.