In the EU FP7 project CogX we have developed a corious robot George; a complex heterogenuous distributed system for interactive learning of visual concepts in a dialogue with a tutor. Our objective was to demonstrate that a cognitive system can efficiently acquire conceptual models in an interactive learning process that is not overly taxing with respect to tutor supervision and is performed in an intuitive, user-friendly way.

In this research work we address the problem of interactive learning of categorical knowledge from the active learning perspective. We describe and implement several teacher and learner-driven approaches that require different levels of teacher competencies and consider different types of knowledge for selection of training samples.

The affordances of the environment are what it offers the robot, what it provides or furnishes, either for good or ill. In this work we developed a self-supervised system that allows for the affordances of novel objects to be broadly classified by observing their respective object property features and using them as input to a classifier trained by the affordance learning algorithm.