Datasets › R2R
R2R (Room-to-Room)
R2R is a dataset for visually-grounded natural language navigation in real buildings. The dataset requires autonomous agents to follow human-generated navigation instructions in previously unseen buildings, as illustrated in the demo above. For training, each instruction is associated with a Matterport3D Simulator trajectory. 22k instructions are available, with an average length of 29 words. There is a test evaluation server for this dataset available at EvalAI.
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Visual Navigation | R2R | SUSA spl 0.6383 | Agent Journey Beyond RGB: Unveiling Hybrid... | HCI-LMC/VLN-SUSA | 11 | Compare |
| Vision-Language Navigation | Room2Room | R2R+EnvDrop spl 0.61 | Learning to Navigate Unseen Environments: Back... | airsplay/R2R-EnvDrop | 3 | Compare |
Papers archive 2025-07-28
13 shown of 13 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 174. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- Room2Room
- R2R
2 variant names, as the archive lists them.
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