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

LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation