  Achieving social acceptance is one of the main goals of Social Robotic
Navigation. Despite this topic has received increasing interest in recent
years, most of the research has focused on driving the robotic agent along
obstacle-free trajectories, planning around estimates of future human motion to
respect personal distances and optimize navigation. However, social
interactions in everyday life are also dictated by norms that do not strictly
depend on movement, such as when standing at the end of a queue rather than
cutting it. In this paper, we propose a novel method to recognize common social
scenarios and modify a traditional planner's cost function to adapt to them.
This solution enables the robot to carry out different social navigation
behaviors that would not arise otherwise, maintaining the robustness of
traditional navigation. Our approach allows the robot to learn different social
norms with a single learned model, rather than having different modules for
each task. As a proof of concept, we consider the tasks of queuing and respect
interaction spaces of groups of people talking to one another, but the method
can be extended to other human activities that do not involve motion.
