CMU FRC seminar: Learning to Select Skills within a Dynamic Environment

Speaker: Brenna Argall, PhD Student, Robotics Institute, Carnegie Mellon University
Date: Thursday, October 20

Abstract:
By augmenting a robot's reasoning with learning, we hope to promote their ability to adapt and respond intelligently within dynamic environments. Our chosen domain is Robocup robot soccer, in which our Segway robots perceive, reason, and act under the highly dynamic and adversarial constraints of a soccer game. In particular, we are interested in applying learning to the question of soccer skill selection; that is, to the choice of which action, or sequence of actions, to execute to attain a specific goal. Expert learning easily extends to this problem, where each expert recommends a single soccer skill. In this talk we introduce our experts learning algorithm, dEXP3, which is a modification on EXP3 (Auer et al., 1995) to enhance flexibility within dynamic environments. The modification present in dEXP3 explicitly handles the case where a previously learned best expert begins to fail. We present our results from implementation both in simulation and on the robots. With comparisons to the foundation algorithm EXP3 we show that, in response to environment variations, our enhanced algorithm exhibits faster adaptability and subsequent better performance.

Speaker Bio:
Brenna Argall is a second year Ph.D. student in the Robotics Institute, affiliated with the CORAL Research Group and co-advised by Dr. Brett Browning and Prof. Manuela Veloso. Her research interests lie with robot autonomy and heterogeneous team coordination, and in particular, with how learning can be used to improve autonomous robot decision making in dynamic environments. Prior to joining the Robotics Institute, Brenna researched in functional brain imaging within the Laboratory of Brain and Cognition at the National Institutes of Health. She received her B.S. in Mathematics in 2002 from Carnegie Mellon University.