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

13 years 11 months ago

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— 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...

Brenna Argall, Brett Browning, Manuela M. Veloso

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