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.

移动机器人在各种拥挤场合中得到广泛应用，社会接受的导航行为是可扩展应用和人类认可的必要条件。深度强化学习方法被用于学习机器人的导航策略和模拟机器人与人类之间的复杂互动。通过在与人类互动的环境中训练深度强化学习策略，我们提出了一种新颖的社会整合导航方法，该方法适应性地产生机器人的社会行为，并且在距离行驶、完成时间和对环境中所有代理的负面影响方面优于社会感知方法。