  Understanding spatial and visual information is essential for a navigation
agent who follows natural language instructions. The current Transformer-based
VLN agents entangle the orientation and vision information, which limits the
gain from the learning of each information source. In this paper, we design a
neural agent with explicit Orientation and Vision modules. Those modules learn
to ground spatial information and landmark mentions in the instructions to the
visual environment more effectively. To strengthen the spatial reasoning and
visual perception of the agent, we design specific pre-training tasks to feed
and better utilize the corresponding modules in our final navigation model. We
evaluate our approach on both Room2room (R2R) and Room4room (R4R) datasets and
achieve the state of the art results on both benchmarks.
