In this paper we test the dominant paradigm for modeling the semantics of determined noun phrases called Generalized Quantifier Theory in embodied interactions with robots. We contrast the traditional approach with a new approach, called Clustering Determination, which is heavily inspired by research on grounding of sensorimotor categories, and we show that our approach performs better in noisy, real world, referential communication.
