  For a robot to be both autonomous and collaborative requires the ability to
adapt its movement to a variety of external stimuli, whether these come from
humans or other robots. Typically, legged robots have oscillation periods
explicitly defined as a control parameter, limiting the adaptability of walking
gaits. Here we demonstrate a virtual quadruped robot employing a bio-inspired
central pattern generator (CPG) that can spontaneously synchronize its movement
to a range of rhythmic stimuli. Multi-objective evolutionary algorithms were
used to optimize the variation of movement speed and direction as a function of
the brain stem drive and the center of mass control respectively. This was
followed by optimization of an additional layer of neurons that filters
fluctuating inputs. As a result, a range of CPGs were able to adjust their gait
pattern and/or frequency to match the input period. We show how this can be
used to facilitate coordinated movement despite differences in morphology, as
well as to learn new movement patterns.
