Benchmark Almanac

Room-Across-Room (RxR)

RxR Mono 41.5% tops its leaderboard
Not in model cards

Agents follow navigation instructions through photo-realistic indoor scenes to reach a goal. The instructions are in English, Hindi and Telugu, and each word is time-aligned to the annotator's position. Scores include success rate and how closely the path follows the reference route.

BenchmarkList links an unrelated paper (arXiv 2103.11372). The RxR paper is arXiv 2010.07954. Most results come from vision-and-language navigation models, not general-purpose models. About 190 GitHub stars, and the repo was last pushed in 2023-07.

Made by
Google Research
Released
October 2020
Kind
Simulated robots
Level
Everyday
Scored
Checked automatically
Answers
Agent task

In the authors’ words

We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators. We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations. We also provide results for a model that learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. The size, scope and detail of RxR dramatically expands the frontier for research on embodied language agents in simulated, photo-realistic environments.

Abstract of Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding (arXiv:2010.07954, CC0)
2
Entries on its own leaderboard, 0 models we track

RxR Mono tops the leaderboard with 41.5%

Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.

  1. 41.5 RxR Mono
  2. 37.2 RxR Multi
  1. 1 RxR Mono 41.5%
  2. 2 RxR Multi 37.2%

Source: Official Room-Across-Room (RxR) leaderboard

#EntryScoreSettingDateSource
1 RxR Mono 41.5% Official leaderboard
2 RxR Multi 37.2% Official leaderboard

No lab model card reports it yet

We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.

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