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 ego navigation performance while significantly reducing the negative impact on all agents within the environment.

翻译：移动机器人正大规模应用于各种拥挤场景，并逐渐成为我们社会的一部分。具备个体人文关怀的移动机器人，其社会可接受的导航行为是实现规模化应用和人类接纳的关键要求。深度强化学习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表现出的社会行为对现有基于深度强化学习的导航方法进行分类，区分为缺乏社会行为的社交避障方法与具有显式预定义社会行为的社会感知方法。此外，我们提出一种新颖的社会融合导航方法，其中机器人的社会行为具有自适应性，并在与人类的交互中自然涌现。我们的方法构建源于社会学定义，即社会行为以他者行为为导向。深度强化学习策略在智能体以社会融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，所提出的社会融合导航方法在自我导航性能方面优于社会感知方法，同时显著降低了对环境中所有智能体的负面影响。