  Robots navigating in human crowds need to optimize their paths not only for
their task performance but also for their compliance to social norms. One of
the key challenges in this context is the lack of standard metrics for
evaluating and optimizing a socially compliant behavior. Existing works in
social navigation can be grouped according to the differences in their
optimization objectives. For instance, the reinforcement learning approaches
tend to optimize on the \textit{comfort} aspect of the socially compliant
navigation, whereas the inverse reinforcement learning approaches are designed
to achieve \textit{natural} behavior. In this paper, we propose NaviGAN, a
generative navigation algorithm that jointly optimizes both of the
\textit{comfort} and \textit{naturalness} aspects. Our approach is designed as
an adversarial training framework that can learn to generate a navigation path
that is both optimized for achieving a goal and for complying with latent
social rules. A set of experiments has been carried out on multiple datasets to
demonstrate the strengths of the proposed approach quantitatively. We also
perform extensive experiments using a physical robot in a real-world
environment to qualitatively evaluate the trained social navigation behavior.
The video recordings of the robot experiments can be found in the link:
https://youtu.be/61blDymjCpw.
