Sharing knowledge with robots

Abstract

Intelligent robots need to share knowledge with human beings for flexible interaction. However, the gap between low‐level sensory data and abstract human knowledge makes it difficult to preencode robot behavior against human's various complex demands. This article presents a way of enabling robots to learn abstract concepts from sensory and perceptual data. In order to overcome the gap between the low‐level sensory data and higher level concept description, a method called feature abstraction is used. Feature abstraction dynamically defines abstract sensors from primitive sensory devices and makes it possible to learn appropriate sensory‐motor constraints. This method has been implemented on a real mobile robot as a learning system called Acorn‐II. Acorn‐II was evaluated with some empirical results and it was shown that the system can learn some abstract concepts more accurately than other existing systems.