We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially-appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot was able to select and account for social objectives related to navigation autonomously.

翻译：我们提出了一个背景分类管道,以使机器人能够根据观察到的社会情景改变其导航战略。社会软件导航考虑到社会行为,以改善人与人之间的导航。大多数现有研究使用不同的技术,将社会规范纳入单一环境的机器人路径规划中。为走廊行为工作的方法可能不利于接近人,等等。我们开发了一个高层决策子系统,一个基于模型的背景分类器,一个基于多目标优化的地方规划器,以实现自主感知环境的社会认知轨迹。机器人可以使用上下文分类系统选择社会目标,这些社会目标后来被Pareto Concavity Reforst(PacET)基于本地规划器的本地规划器用于产生安全、舒适、社会上适合的环境轨迹。这在Pioneer移动机器人平台的多个环境中得到了测试和验证;结果显示机器人能够选择和核算与自主导航相关的社会目标。