  We study the problem of jointly reasoning about language and vision through a
navigation and spatial reasoning task. We introduce the Touchdown task and
dataset, where an agent must first follow navigation instructions in a
real-life visual urban environment, and then identify a location described in
natural language to find a hidden object at the goal position. The data
contains 9,326 examples of English instructions and spatial descriptions paired
with demonstrations. Empirical analysis shows the data presents an open
challenge to existing methods, and qualitative linguistic analysis shows that
the data displays richer use of spatial reasoning compared to related
resources.
