Robots acting in real-world environments usually interact with humans. Interactions may occur at different levels of abstraction (e.g., process, task, physical), entailing different research challenges (e.g., task allocation, human-robot joint actions, robot navigation). When acting in social situations, robots should recognize the context and behave in different manners, so as to act and interact in a correct and acceptable way. We propose the integration of task and motion planning to contextualize robot behaviors for social navigation. The main idea is to leverage the contextual knowledge of a deliberative task planner to dynamically adapt the navigation behaviors of a robot and enhance humanrobot interaction. More specifically, we propose a holistic model of tasks and human features and a mapping from task-level knowledge to motion-level knowledge to constrain the generation of robot trajectories. The proposed framework is tested in simulation for some commonly occurring scenarios in a hospital.