Hybrid learning architecture for fuzzy control of quadruped walking robots

Abstract

This article presents a hybrid learning architecture for fuzzy control of quadruped walking robots in the RoboCup domain. It combines reactive behaviors with deliberative reasoning to achieve complex goals in uncertain and dynamic environments. To achieve real-time and robust control performance, fuzzy logic controllers (FLCs) are used to encode the behaviors and a two-stage learning scheme is adopted to make these FLCs be adaptive to complex situations. The first stage is called structure learning, in which the rule base of an FLC is generated by a Q-learning scheme. The second stage is called parameter learning, in which the parameters of membership functions in input fuzzy sets are learned by using a real value genetic algorithm. The experimental results are provided to show the suitability of the architecture and effectiveness of the proposed learning scheme. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 131–152, 2005.