  Robot crowd navigation has been gaining increasing attention and popularity
in various practical applications. In existing research, deep reinforcement
learning has been applied to robot crowd navigation by training policies in an
online mode. However, this inevitably leads to unsafe exploration, and
consequently causes low sampling efficiency during pedestrian-robot
interaction. To this end, we propose an offline reinforcement learning based
robot crowd navigation algorithm by utilizing pre-collected crowd navigation
experience. Specifically, this algorithm integrates a spatial-temporal state
into implicit Q-Learning to avoid querying out-of-distribution robot actions of
the pre-collected experience, while capturing spatial-temporal features from
the offline pedestrian-robot interactions. Experimental results demonstrate
that the proposed algorithm outperforms the state-of-the-art methods by means
of qualitative and quantitative analysis.
