Central pattern generators evolved for real-time adaptation to rhythmic stimuli

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 centre 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.

Automated summary

To achieve both autonomy and collaboration, a robot needs to adapt its movement to various stimuli. Legged robots with defined oscillation periods often lack adaptability. In this study, a virtual quadruped robot with a bio-inspired central pattern generator was developed. The robot can spontaneously synchronize its movement to rhythmic stimuli and adjust its gait pattern and frequency accordingly. This technology enables coordinated movement despite differences in morphology and facilitates learning of new movement patterns.