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.

翻译：实现社会接受度是社交机器人导航的主要目标之一。尽管近年来该主题受到越来越多的关注，但大多数研究都集中于驱动机器人沿无障碍轨迹移动，围绕未来人类运动的估计进行规划以尊重个人距离并优化导航。然而，日常生活中的社交互动也受不严格依赖于运动的规范所支配，例如排队时站在队尾而非插队。本文提出一种识别常见社交场景并修改传统规划器成本函数以适应这些场景的新方法。该方案使机器人能够执行原本不会出现的不同社交导航行为，同时保持传统导航的鲁棒性。我们的方法允许机器人通过单一学习模型学习不同的社会规范，而无需为每个任务设置不同模块。作为概念验证，我们考虑了排队和尊重交谈人群互动空间的任务，但该方法可扩展至其他不涉及运动的人类活动。