  Developing robotic technologies for use in human society requires ensuring
the safety of robots' navigation behaviors while adhering to pedestrians'
expectations and social norms. However, maintaining real-time communication
between robots and pedestrians to avoid collisions can be challenging. To
address these challenges, we propose a novel socially-aware navigation
benchmark called NaviSTAR, which utilizes a hybrid Spatio-Temporal grAph
tRansformer (STAR) to understand interactions in human-rich environments fusing
potential crowd multi-modal information. We leverage off-policy reinforcement
learning algorithm with preference learning to train a policy and a reward
function network with supervisor guidance. Additionally, we design a social
score function to evaluate the overall performance of social navigation. To
compare, we train and test our algorithm and other state-of-the-art methods in
both simulator and real-world scenarios independently. Our results show that
NaviSTAR outperforms previous methods with outstanding performance\footnote{The
source code and experiment videos of this work are available at:
https://sites.google.com/view/san-navistar
