  Entrainment of movement to a periodic stimulus is a characteristic
intelligent behaviour in humans and an important goal for adaptive robotics. We
demonstrate a quadruped central pattern generator (CPG), consisting of modified
Matsuoka neurons, that spontaneously adjusts its period of oscillation to that
of a periodic input signal. This is done by simple forcing, with the aid of a
filtering network as well as a neural model with tonic input-dependent
oscillation period. We first use the NSGA3 algorithm to evolve the CPG
parameters, using separate fitness functions for period tunability, limb
homogeneity and gait stability. Four CPGs, maximizing different weighted
averages of the fitness functions, are then selected from the Pareto front and
each is used as a basis for optimizing a filter network. Different numbers of
neurons are tested for each filter network. We find that period tunability in
particular facilitates robust entrainment, that bounding gaits entrain more
easily than walking gaits, and that more neurons in the filter network are
beneficial for pre-processing input signals. The system that we present can be
used in conjunction with sensory feedback to allow low-level adaptive and
robust behaviour in walking robots.
