Selection of Behavioral Parameters: Integration of Discontinuous Switching via Case-Based Reasoning with Continuous Adaptation via Learning Momentum
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Abstract
This paper studies the effects of the integration of
two learning algorithms, Case-Based Reasoning (CBR)
and Learning Momentum (LM), for the selection of
behavioral parameters in real-time for robotic
navigational tasks. Use of CBR methodology in the
selection of behavioral parameters has already shown
significant improvement in robot performance [3, 6, 7,
14] as measured by mission completion time and success
rate. It has also made unnecessary the manual
configuration of behavioral parameters from a user.
However, the choice of the library of CBR cases does
affect the robot's performance, and choosing the right
library sometimes is a difficult task especially when
working with a real robot. In contrast, Learning
Momentum does not depend on any prior information
such as cases and searches for the "right" parameters in
real-time. This results in high mission success rates and
requires no manual configuration of parameters, but it
shows no improvement in mission completion time [2].
This work combines the two approaches so that CBR
discontinuously switches behavioral parameters based on
given cases whereas LM uses these parameters as a
starting point for the real-time search for the "right"
parameters. The integrated system was extensively
evaluated on both simulated and physical robots. The tests
showed that on simulated robots the integrated system
performed as well as the CBR only system and
outperformed the LM only system, whereas on real robots
it significantly outperformed both CBR only and LM only
systems.
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2001
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