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

TL;DR

This paper presents a bio-inspired central pattern generator for a virtual quadruped robot that can adapt its gait to rhythmic stimuli, enabling flexible and synchronized movement in dynamic environments.

Contribution

The study introduces a CPG model optimized with evolutionary algorithms that can spontaneously synchronize with external rhythmic stimuli, enhancing robot adaptability.

Findings

CPGs can adjust gait patterns and frequency to match input stimuli

The approach enables coordinated movement despite morphological differences

The method facilitates learning new movement patterns

Abstract

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.