Learning to Select State Machines using Expert Advice on an Autonomous Robot

— Hierarchical state machines have proven to be a powerful tool for controlling autonomous robots due to their flexibility and modularity. For most real robot implementations, however, it is often the case that the control hierarchy is hand-coded. As a result, the development process is often time intensive and error prone. In this paper, we explore the use of an experts learning approach, based on Auer and colleagues’ Exp3 [1], to help overcome some of these limitations. In particular, we develop a modified learning algorithm, which we call rExp3, that exploits the structure provided by a control hierarchary by treating each state machine as an ’expert’. Our experiments validate the performance of rExp3 on a real robot performing a task, and demonstrate that rExp3 is able to quickly learn to select the best state machine expert to execute. Through our investigations in these environments, we identify a need for faster learning recovery when the relative performances of exper...

• Type: Conference
• Year: 2007
• Where: ICRA
• Authors: Brenna Argall, Brett Browning, Manuela M. Veloso
• Keywords: ICRA 2007, Real Robot, Real Robot Implementations, Robotics, State Machine