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Learning Central Pattern Generator Network with Back-Propagation Algorithm

An adaptable central pattern generator (CPG) that directly controls the rhythmic motion of multilegged robot must combine plasticity and sustainable periodicity. This combination requires an algorithm that searches the parametric space of the CPG and yields a non-stationary and non-divergent solution. We model the CPG with the pioneering Matsuoka’s neural oscillator which is (mostly) non-divergent and provides constraints ensuring nonstationarity. We embed these constraints into the CPG formulation which we further implemented as a layer of an artificial neural network. This enables the CPG to be learnable by back-propagation algorithm while sustaining the desirable properties. Moreover, the proposed CPG can be integrated into more complex networks and trained under different optimization objectives. In addition to the theoretical properties of the developed system, its flexibility is demonstrated in successful learning of the tripod motion gait with its practical deployment on the real hexapod walking robot.