Fast learning mapping schemes for robotic hand-eye coordination

Abstract

In aiming for advanced robotic systems that autonomously and permanently readapt to changing and uncertain environments,we introduce a scheme of fast learning and readaptation of robotic sensorimotor mappings based on biological mechanisms underpinning the development and maintenance of accurate human reaching. The study presents a range of experiments, using two distinct computational architectures, on both learning and realignment of robotic hand-eye coordination. Analysis of the results provide insights into the putative parameters and mechanisms required for fast readaptation and generalization from both a robotic and biological perspective.