  Socially aware robot navigation, where a robot is required to optimize its
trajectory to maintain comfortable and compliant spatial interactions with
humans in addition to reaching its goal without collisions, is a fundamental
yet challenging task in the context of human-robot interaction. While existing
learning-based methods have achieved better performance than the preceding
model-based ones, they still have drawbacks: reinforcement learning depends on
the handcrafted reward that is unlikely to effectively quantify broad social
compliance, and can lead to reward exploitation problems; meanwhile, inverse
reinforcement learning suffers from the need for expensive human
demonstrations. In this paper, we propose a feedback-efficient active
preference learning approach, FAPL, that distills human comfort and expectation
into a reward model to guide the robot agent to explore latent aspects of
social compliance. We further introduce hybrid experience learning to improve
the efficiency of human feedback and samples, and evaluate benefits of robot
behaviors learned from FAPL through extensive simulation experiments and a user
study (N=10) employing a physical robot to navigate with human subjects in
real-world scenarios. Source code and experiment videos for this work are
available at:https://sites.google.com/view/san-fapl.
