  In this letter, we propose a robust and fast navigation system in a narrow
indoor environment for UGV (Unmanned Ground Vehicle) using 2D LiDAR and
odometry. We used behavior cloning with Transformer neural network to learn the
optimization-based baseline algorithm. We inject Gaussian noise during expert
demonstration to increase the robustness of learned policy. We evaluate the
performance of LiCS using both simulation and hardware experiments. It
outperforms all other baselines in terms of navigation performance and can
maintain its robust performance even on highly cluttered environments. During
the hardware experiments, LiCS can maintain safe navigation at maximum speed of
$1.5\ m/s$.
