  This paper presents a bio-inspired central pattern generator (CPG)-type
architecture for learning optimal maneuvering control of periodic locomotory
gaits. The architecture is presented here with the aid of a snake robot model
problem involving planar locomotion of coupled rigid body systems. The maneuver
involves 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 posterior
distribution that is used to construct the optimal control input for
maneuvering the robot. A Q-learning algorithm is applied to learn the
approximate optimal control law. The issues surrounding the parametrization of
the Q-function are discussed. The theoretical results are illustrated with
numerics for a 5-link snake robot system.
