  We deal with the navigation problem where the agent follows natural language
instructions while observing the environment. Focusing on language
understanding, we show the importance of spatial semantics in grounding
navigation instructions into visual perceptions. We propose a neural agent that
uses the elements of spatial configurations and investigate their influence on
the navigation agent's reasoning ability. Moreover, we model the sequential
execution order and align visual objects with spatial configurations in the
instruction. Our neural agent improves strong baselines on the seen
environments and shows competitive performance on the unseen environments.
Additionally, the experimental results demonstrate that explicit modeling of
spatial semantic elements in the instructions can improve the grounding and
spatial reasoning of the model.
