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Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments

by   Howard Chen, et al.

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 to a goal position, and then identify in the observed image a location described in natural language to find a hidden object. The data contains 9,326 examples of English instructions and spatial descriptions paired with demonstrations. We perform qualitative linguistic analysis, and show that the data displays richer use of spatial reasoning compared to related resources. Empirical analysis shows the data presents an open challenge to existing methods.


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Code Repositories


Cornell Touchdown natural language navigation and spatial reasoning dataset.

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Cornell Instruction Following Framework

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Implementation of "Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation"

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Cloned from

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