  Since humans and robots are increasingly sharing portions of their
operational spaces, experimental evidence is needed to ascertain the safety and
social acceptability of robots in human-populated environments. Although
several studies have aimed at devising strategies for robot trajectory planning
to perform \emph{safe} motion in populated environments, a few efforts have
\emph{measured} to what extent a robot trajectory is \emph{accepted} by humans.
Here, we present a navigation system for autonomous robotics that ensures
safety and social acceptability of robotic trajectories. We overcome the
typical reactive nature of state-of-the-art trajectory planners by leveraging
non-cooperative game theory to design a planner that encapsulates human-like
features of preservation of a vital space, recognition of groups, sequential
and strategized decision making, and smooth obstacle avoidance. Social
acceptability is measured through a variation of the Turing test administered
in the form of a survey questionnaire to a pool of 691 participants. Comparison
terms for our tests are a state-of-the-art navigation algorithm (Enhanced
Vector Field Histogram, VFH) and purely human trajectories. While all
participants easily recognized the non-human nature of VFH-generated
trajectories, the distinction between game-theoretical trajectories and human
ones were hardly revealed. These results mark a strong milestone toward the
full integration of robots in social environments.
