In the future, eventually, robots will become extremely widespread also in urban environments, and perhaps, us humans will need to learn how to interact and live with them. Social navigation accounts for the problem of having a safe and efficient navigation among objects and pedestrians, which can be considered as sentient road users and, for this reason, more special considerations need be taken into account when dealing with them. The goal of any social navigation software stack is to make the robotic agent behave as similarly as possible to a pedestrian, which is used to abide to many social rules that has learnt throughout all of their life. In this way, humans will not need to learn new "robotic" rules for navigating an environment: they would only need to apply the same rules that also robots will follow. Many social navigation approaches rely on sociological-psychological studies in which the pedestrian motion has been modeled in deep details. In this work a novel approach is presented, leveraging the predictivity of Model Predictive Control and the reactivity of Social Force Model, which will model the pedestrian motion.