Socially compliant navigation is an integral part of safety features in Human-Robot Interaction. Traditional approaches to mobile navigation prioritize physical aspects, such as efficiency, but social behaviors gain traction as robots appear more in daily life. Recent techniques to improve the social compliance of navigation often rely on predefined features or reward functions, introducing assumptions about social human behavior. To address this limitation, we propose a novel Learning from Demonstration (LfD) framework for social navigation that exclusively utilizes raw sensory data. Additionally, the proposed system contains mechanisms to consider the future paths of the surrounding pedestrians, acknowledging the temporal aspect of the problem. The final product is expected to reduce the anxiety of people sharing their environment with a mobile robot, helping them trust that the robot is aware of their presence and will not harm them. As the framework is currently being developed, we outline its components, present experimental results, and discuss future work towards realizing this framework.

翻译：社会合规导航是人机交互中安全功能的重要组成部分。传统的移动导航方法优先考虑物理方面（如效率），但随着机器人在日常生活中越来越常见，社交行为日益受到关注。近年来改进导航社会合规性的技术通常依赖预定义特征或奖励函数，这引入了关于人类社交行为的假设。为解决这一局限性，我们提出了一种新型的社会导航学习示范（LfD）框架，该框架仅利用原始感官数据。此外，所提议系统包含考虑周围行人未来轨迹的机制，以处理该问题的时间维度。最终成果有望减轻人们与移动机器人共享环境时的焦虑，帮助他们信任机器人能够意识到自身存在且不会造成伤害。目前该框架正在开发中，我们概述了其组件，展示了实验结果，并讨论了实现该框架的未来工作方向。