Transfer of human skills to neural net robot controllers

Abstract

The focus of this study is to examine the teaching data for training the neural network: whether or not the sample data provide a consistent mapping from inputs to outputs, whether some significant information is missing in the measurement of human operations, and whether the network may converge to the global minimum where the network produces a correct mapping. Conditions for a given data sample to satisfy in order to generate a consistent mapping are obtained by using Lipschitz's condition, which is known as a condition for the continuity of functions. Prior to the training of neural networks, sample data are examined and validated with Lipschitz's condition, which guarantees the consistency. This validation method is applied to a skill transfer problem of deburring robots in order to demonstrate the approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>