Learning Behavioral Parameterization Using Spatio-Temporal Case-Based Reasoning

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Date
2001Author
Arkin, Ronald C.
Kaess, Michael
Likhachev, Maxim
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This paper presents an approach to learning an optimal
behavioral parameterization in the framework of a Case-Based
Reasoning methodology for autonomous navigation tasks. It is
based on our previous work on a behavior-based robotic system
that also employed spatio-temporal case-based reasoning [3] in
the selection of behavioral parameters but was not capable of
learning new parameterizations. The present method extends the
case-based reasoning module by making it capable of learning
new and optimizing the existing cases where each case is a set of
behavioral parameters. The learning process can either be a
separate training process or be part of the mission execution. In
either case, the robot learns an optimal parameterization of its
behavior for different environments it encounters. The goal of this
research is not only to automatically optimize the performance of
the robot but also to avoid the manual configuration of behavioral
parameters and the initial configuration of a case library, both of
which require the user to possess good knowledge of robot
behavior and the performance of numerous experiments. The
presented method was integrated within a hybrid robot
architecture and evaluated in extensive computer simulations,
showing a significant increase in the performance over a nonadaptive
system and a performance comparable to a non-learning
CBR system that uses a hand-coded case library.