Bioinspired Decentralized Hexapod Control with a Graph Neural Network

Abstract

Legged locomotion enables animals to navigate challenging terrains. However, it demands intricate coordination between the legs, with varying levels of information exchange depending on the task. For instance, in more demanding scenarios such as an insect climbing on a twig, greater coordination between the legs is necessary to achieve adaptive behavior. To address this challenge for legged robots, we present a concept and preliminary results of a decentralized biologically inspired controller for a hexapod robot: Based on insights of coordination influences between legs in stick insects, our approach models inter-leg information flow as message passing through a Graph Neural Network.