Artificial Semantic Memory with Autonomous Learning Applied to Social Robots

Abstract

Semantic memory stores knowledge about the meanings of words and the relationships between these meanings. In recent years, Artificial Intelligence, in particular Deep Learning, has successfully resolved the identification of classes of elements in images, and even instances of a class, providing a basic form of semantic memory. Unfortunately, incorporating new instances of a class requires a complex and long process of labeling and offline training. We are convinced that the combination of convolutional networks and statistical classifiers allows us to create a long-term semantic memory that is capable of learning online. To validate this hypothesis, we have implemented a long-term semantic memory in a social robot. The robot initially only recognizes people, but, after interacting with different people, is able to distinguish them from each other. The advantage of our approach is that the process of long-term memorization is done autonomously without the need for offline processing.