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Robots navigating in human crowds need to optimize their paths not only fortheir task performance but also for their compliance to social norms. One ofthe key challenges in this context is the lack of standard metrics forevaluating and optimizing a socially compliant behavior. Existing works insocial navigation can be grouped according to the differences in theiroptimization objectives. For instance, the reinforcement learning approachestend to optimize on the \textit{comfort} aspect of the socially compliantnavigation, whereas the inverse reinforcement learning approaches are designedto achieve \textit{natural} behavior. In this paper, we propose NaviGAN, agenerative navigation algorithm that jointly optimizes both of the\textit{comfort} and \textit{naturalness} aspects. Our approach is designed asan adversarial training framework that can learn to generate a navigation paththat is both optimized for achieving a goal and for complying with latentsocial rules. A set of experiments has been carried out on multiple datasets todemonstrate the strengths of the proposed approach quantitatively. We alsoperform extensive experiments using a physical robot in a real-worldenvironment 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.

NaviGAN: A Generative Approach for Socially Compliant Navigation