This article presents an integrated robot system capable of interactive learning in dialogue with a human. Such a system needs to have several competencies and must be able to process different types of representations. In this article, we describe a collection of mechanisms that enable integration of heterogeneous competencies in a principled way. Central to our design is the creation of beliefs from visual and linguistic information, and the use of these beliefs for planning system behaviour to satisfy internal drives. The system is able to detect gaps in its knowledge and to plan and execute actions that provide information needed to fill these gaps. We propose a hierarchy of mechanisms which are capable of engaging in different kinds of learning interactions, e.g. those initiated by a tutor or by the system itself. We present the theory these mechanisms are build upon and an instantiation of this theory in the form of an integrated robot system. We demonstrate the operation of the system in the case of learning conceptual models of objects and their visual properties.

In this paper we present representations and mechanisms that facilitate continuous learning of visual concepts in dialogue with a tutor and show the implemented robot system. We present how beliefs about the world are created by processing visual and linguistic information and show how they are used for planning system behaviour with the aim at satisfying its internal drive - to extend its knowledge. The system facilitates different kinds of learning initiated by the human tutor or by the system itself. We demonstrate these principles in the case of learning about object colours and basic shapes.

This paper presents an implemented approach of processing situated dialogue between a human and a robot. The focus is on task-oriented dialogue, set in the larger context of human-robot collaborative activity. The approach models understanding and production of dialogue to include intension (what is being talked about), intention (the goal of why something is being said), and attention (what is being focused on). These dimensions are directly construed in terms of assumptions and assertions on situated multi-agent belief models. The approach is continual in that it allows for interpretations to be dynamically retracted, revised, or deferred. This makes it possible to deal with the inherent asymmetry in how robots and humans tend to understand dialogue, and the world in which it is set. The approach has been fully implemented, and integrated into a cognitive robot. The paper discusses the implementation, and illustrates it in a collaborative learning setting.