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：社交合规导航是人机交互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层面如效率，但随着机器人越来越多地出现在日常生活中，社交行为逐渐受到重视。最近改善导航社交合规性的技术通常依赖于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，我们提出了一种全新的基于示范学习（LfD）社交导航框架，该框架仅利用原始传感器数据。此外，所提出的系统包含考虑周围行人未来路径的机制，从而承认问题的时间维度。最终成果有望减少人类与移动机器人共处环境时的焦虑，帮助人们相信机器人能感知其存在且不会造成伤害。由于该框架尚在开发中，我们概述了其组件、展示了实验结果，并讨论了实现这一框架的未来工作方向。