Selection of Behavioral Parameters: Integration of Discontinuous Switching via Case-Based Reasoning with Continuous Adaptation via Learning Momentum
J. Brian Lee, Maxim Likhachev, Ronald C. Arkin
Mobile Robot Laboratory
College of Computing, Georgia Institute of Technology
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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 . 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.
Index terms: Learning Momentum, Case-Based Reasoning, Behavior-Based Robotics, Reactive Robotics.