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.


page 4

page 14

page 15

page 16

page 17

page 18

page 19

page 20


Towards Navigation by Reasoning over Spatial Configurations

We deal with the navigation problem where the agent follows natural lang...

VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View

Incremental decision making in real-world environments is one of the mos...

A Corpus for Reasoning About Natural Language Grounded in Photographs

We introduce a new dataset for joint reasoning about language and vision...

Learning Interpretable Spatial Operations in a Rich 3D Blocks World

In this paper, we study the problem of mapping natural language instruct...

HeGeL: A Novel Dataset for Geo-Location from Hebrew Text

The task of textual geolocation - retrieving the coordinates of a place ...

RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation

Following navigation instructions in natural language requires a composi...

Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

In the vision-and-language navigation (VLN) task, an agent follows natur...

Code Repositories


Cornell Touchdown natural language navigation and spatial reasoning dataset.

view repo


Cornell Instruction Following Framework

view repo


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

view repo


Cloned from

view repo

Please sign up or login with your details

Forgot password? Click here to reset