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

CVPR 2019  ·  Howard Chen, Alane Suhr, Dipendra Misra, Noah Snavely, Yoav Artzi ·

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.

PDF Abstract CVPR 2019 PDF CVPR 2019 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Vision and Language Navigation Touchdown Dataset RConcat Task Completion (TC) 10.7 # 10
Compare
Vision and Language Navigation Touchdown Dataset Gated Attention (GA) Task Completion (TC) 5.5 # 11
Compare

Methods
Add Remove

No methods listed for this paper. Add relevant methods here
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy