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This paper presents a bio-inspired central pattern generator (CPG)-typearchitecture for learning optimal maneuvering control of periodic locomotorygaits. The architecture is presented here with the aid of a snake robot modelproblem involving planar locomotion of coupled rigid body systems. The maneuverinvolves clockwise or counterclockwise turning from a nominally straight path.The CPG circuit is realized as a coupled oscillator feedback particle filter.The collective dynamics of the filter are used to approximate a posteriordistribution that is used to construct the optimal control input formaneuvering the robot. A Q-learning algorithm is applied to learn theapproximate optimal control law. The issues surrounding the parametrization ofthe Q-function are discussed. The theoretical results are illustrated withnumerics for a 5-link snake robot system.

Bio-inspired Learning of Sensorimotor Control for Locomotion