In this paper, we study if modeling can help discriminate actions which in turn can be used to select an appropriate behavior for a mobile robot. For human-human interaction, significant social and communicative information can be derived from interpersonal distance between two or more people. If Human-Robot Interaction reflects this human-human interaction property, then interpersonal distance between a human and a robot may contain similar social and communicative information. An effective robot's actions, including actions associated with interpersonal distance, must be suitable for a given social circumstance. Studying interpersonal distance between a robot and a human is very important for assistive robots. We use autonomously detected features to develop such an interpersonal model using Gaussian Mixture Model (GMM) and demonstrate that such a learned model can discriminate different human actions. The proposed approach can be used in a socially-aware planner to weight trajectories and select actions that are socially appropriate for a given social situation.