  Mobile robot navigation has seen extensive research in the last decades. The
aspect of collaboration with robots and humans sharing workspaces will become
increasingly important in the future. Therefore, the next generation of mobile
robots needs to be socially-compliant to be accepted by their human
collaborators. However, a formal definition of compliance is not
straightforward. On the other hand, empowerment has been used by artificial
agents to learn complicated and generalized actions and also has been shown to
be a good model for biological behaviors. In this paper, we go beyond the
approach of classical \acf{RL} and provide our agent with intrinsic motivation
using empowerment. In contrast to self-empowerment, a robot employing our
approach strives for the empowerment of people in its environment, so they are
not disturbed by the robot's presence and motion. In our experiments, we show
that our approach has a positive influence on humans, as it minimizes its
distance to humans and thus decreases human travel time while moving
efficiently towards its own goal. An interactive user-study shows that our
method is considered more social than other state-of-the-art approaches by the
participants.

Multiagent Systems;Artificial Intelligence;Machine Learning