Socially Integrated Navigation: A Social Acting Robot with Deep
  Reinforcement Learning
Daniel Flögel Lars Fischer Thomas Rudolf Tobias Schürmann Sören Hohmann
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.