Mobile robots are being used on a large scale in various crowded situations and become part of our society. The socially acceptable navigation behavior of a mobile robot with individual human consideration is an essential requirement for scalable applications and human acceptance. Deep Reinforcement Learning (DRL) approaches are recently used to learn a robot's navigation policy and to model the complex interactions between robots and humans. We propose to divide existing DRL-based navigation approaches based on the robot's exhibited social behavior and distinguish between social collision avoidance with a lack of social behavior and socially aware approaches with explicit predefined social behavior. In addition, we propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans. The formulation of our approach is derived from a sociological definition, which states that social acting is oriented toward the acting of others. The DRL policy is trained in an environment where other agents interact socially integrated and reward the robot's behavior individually. The simulation results indicate that the proposed socially integrated navigation approach outperforms a socially aware approach in terms of distance traveled, time to completion, and negative impact on all agents within the environment.

翻译：移动机器人正被大规模应用于各种拥挤场景，并成为我们社会的一部分。具备个体化人类考量的社会可接受导航行为是可扩展应用与人类接纳的关键需求。深度强化学习（DRL）方法近期被用于学习机器人的导航策略，并建模机器人与人类之间的复杂交互。我们提出依据机器人展现的社交行为对现有基于DRL的导航方法进行分类，区分缺乏社交行为的社交避障方法与具有显式预设社交行为的社交感知方法。此外，我们提出一种新颖的社会整合式导航方法，其中机器人的社交行为具有自适应性，并在与人类的交互中涌现。该方法的设计源于社会学定义：社交行为是以他人行为为导向的。DRL策略在一种其他智能体以社会整合方式交互、并对机器人行为进行个体化奖励的环境中进行训练。仿真结果表明，所提出的社会整合式导航方法在行驶距离、任务完成时间以及对环境内所有智能体的负面影响方面均优于社交感知方法。