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AI2-THOR

Introduced by Eric Kolve et al. in AI2-THOR: An Interactive 3D Environment for Visual AI1 Jan 2017 archive 2025-07-28

AI2-Thor is an interactive environment for embodied AI. It contains four types of scenes, including kitchen, living room, bedroom and bathroom, and each scene includes 30 rooms, where each room is unique in terms of furniture placement and item types. There are over 2000 unique objects for AI agents to interact with.

Source: Learning Object Relation Graph andTentative Policy for Visual Navigation Image Source: https://ai2thor.allenai.org/

Benchmarks archive 2025-07-28

All 1 leaderboard 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)PaperCode
Visual Navigation AI2-THOR MVV-IN SPL (All) 17.27 Multimodal Aggregation Approach for Memory Vision-Voice... — 2 Compare

Papers archive 2025-07-28

2 shown of 2 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 243. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning 0 1 1 Sep 2020 not harvested
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning 2 1 3 Dec 2018 ran 1 of 4 samples (3 unverified)

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

Unknown

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • AI2-THOR

1 variant name, as the archive lists them.

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