This paper presents a POMDP-based dialogue system for multimodal human-robot interaction (HRI). Our aim is to exploit a dialogical paradigm to allow a natural and robust interaction between the human and the robot. The proposed dialogue system should improve the robustness and the flexibility of the overall interactive system, including multimodal fusion, interpretation, and decision-making. The dialogue is represented as a Partially Observable Markov Decision Process (POMDPs) to cast the inherent communication ambiguity and noise into the dialogue model. POMDPs have been used in spoken dialogue systems, mainly for tourist information services, but their application to multimodal human-robot interaction is novel. This paper presents the proposed model for dialogue representation and the methodology used to compute a dialogue strategy. The whole architecture has been integrated on a mobile robot platform and has bee n tested in a human-robot interaction scenario to assess the overall performances with respect to baseline controllers. © 2013 ACM.