An integrated system for interactive continuous learning of categorical knowledge

Danijel Skocaj, Alen Vrecko, Marko Mahnic, Miroslav Janícek, Geert-Jan M Kruijff, Marc Hanheide, Nick Hawes, Jeremy L Wyatt, Thomas Keller, Kai Zhou, Michael Zillich, Matej Kristan

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
336 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)823-848
Number of pages26
JournalJournal of Experimental and Theoretical Artificial Intelligence
Issue number5
Early online date5 Feb 2016
Publication statusE-pub ahead of print - 5 Feb 2016


  • Cognitive system
  • interactive learning
  • motive management
  • knowledge gap detection
  • extrospection
  • introspection


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