Title: Teaching a Robot to Avoid Obstacles
Speaker: Bradley Hamner, FRC Staff / Masters Student, Robotics Institute, Carnegie Mellon University
Date: Thursday, May 4, 2006
Time: Noon
Location: NSH 1109
Abstract:
Many obstacle avoidance methods have been presented in the literature, all of which rely on tuning a set of parameters to a control function. Frequently, programmers tune gains by hand until the robot behaves as desired, a nonintuitive and frustrating process. In this talk I will present a method of learning the gains of an obstacle avoidance system automatically by observing how a human operator manually drives the vehicle. I will present an obstacle avoidance algorithm, and its parameters, and show how parameters learned by our method outperform parameters which were hand-tuned. I will also show preliminary results from learning the parameters for multiple vehicles which perform in different environments.
Speaker Bio:
Brad Hamner received a B.S. in Mathematics from Carnegie Mellon University in 2002. Since then he has worked as a staff member in the Field Robotics Center. He entered the Robotics Institute masters program in 2005. His research interests include mobile robot navigation and obstacle avoidance.
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