Fast Learning Mapping Schemes for Robotic Hand–Eye Coordination

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.

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