Self-Organizing Maps for Supervision in Robot Pick-And-Place Operations

Abstract

A scheme for supervised learning based on multiple self-organizing maps (SOMs) is presented, and its application to robotic tasks, namely pick-and-place operations, is outlined. The advantage of this multiple organization is that the learning method is simplified because the problem is divided into several SOMs, which are trained in the standard unsupervised way. The resulting network preserves the SOM properties like dimensionality reduction and cluster formation, and its classification performance is comparable to other supervised methods like backpropagation networks.