Learning diverse motor patterns with a single multi-layered multi-pattern CPG for a humanoid robot

Shoubhik Debnath, John Nassour, Gordon Cheng

Abstract: This paper presents a Multi-Layered Multi-Pattern Central Pattern Generator (CPG) that provides humanoid robots the ability to generate motor patterns in order to perform various upper body tasks (like: reaching and writing). This CPG has two control levels: 1) one for pattern formation (coordination); and 2) another for pattern generation (selection). A unique feature of this CPG is its ability to generate oscillatory, semi-oscillatory, and non-periodic patterns locally, simply through descending control. With a simple learning method the NAO humanoid robot was able to learn how to coordinate motor patterns at different joints in writing numbers from 0 to 9. With a neural-based structure, which separate between the coordination and the selection control levels, our approach is shown to be robust during the execution even with a noisy proprioception (sensory) feedback and also with noisy coordination (pattern formation descending control) signals.