论文标题
学习机器人导航的团体感知政策
Learning a Group-Aware Policy for Robot Navigation
论文作者
论文摘要
人类意识的机器人导航承诺，移动机器人为普通环境中的人们提供多功能援助。虽然先前的研究主要集中在为行人建模为独立的，有意的个人，但人们会分组移动。因此，移动机器人在围绕人导航时必须尊重人类群体。本文使用深入的强化学习探讨了基于动态群体形成的学习群体感知导航政策。通过模拟实验，我们表明，与忽略人类群体的基线政策相比，群体感知政策，实现更大的机器人导航绩效（例如，碰撞较少），最大程度地减少违反社会规范和不适感，并减少机器人对行人的运动影响。我们的结果有助于发展社会导航以及将移动机器人整合到人类环境中。
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional individuals, people move in groups; consequently, it is imperative for mobile robots to respect human groups when navigating around people. This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning. Through simulation experiments, we show that group-aware policies, compared to baseline policies that neglect human groups, achieve greater robot navigation performance (e.g., fewer collisions), minimize violation of social norms and discomfort, and reduce the robot's movement impact on pedestrians. Our results contribute to the development of social navigation and the integration of mobile robots into human environments.
