AgiBot World Challenge 2026 - Datasets
Dear participants,
We are excited to announce that the datasets for AgiBot World Challenge 2026 have been updated.
Track 1: Reasoning2Action
The Reasoning to Action track evaluates models' capabilities in reasoning and action prediction, comprising both online and onsite phases. This track encompasses 10 progressively challenging tasks, ranging from basic to complex, including dual-arm collaboration, long-horizon operations, and high-precision manipulations such as logistics sorting, office organization, retail operations, and daily services.
Based on G2 robot, AgiBot World open datasets, and Genie Sim 3.0, this track focuses on bridging the Sim2Real gap and achieving robust generalization from open-vocabulary understanding to physical interaction.
Competition Tasks
| No. | Task Name |
|---|---|
| 1 | clean_the_desktop |
| 2 | hold_pot |
| 3 | open_door |
| 4 | place_block_into_box |
| 5 | pour_workpiece |
| 6 | scoop_popcorn |
| 7 | sorting_packages |
| 8 | sorting_packages_continuous |
| 9 | stock_and_straighten_shelf |
| 10 | take_wrong_item_shelf |
Dataset Structure
The dataset repository contains three main directories at the top level (to lower the barrier to entry, we also provided a version of the same dataset without depth):
Reasoning2Action-Sim/
βββ clean_the_desktop_part_1/
β βββ data.tar.gz.000
β βββ meta.tar.gz.000
β βββ videos.tar.gz.000
β βββ ...
β βββ videos.tar.gz.013
βββ clean_the_desktop_part_2/
β βββ data.tar.gz.000
β βββ meta.tar.gz.000
β βββ videos.tar.gz.000
β βββ ...
β βββ videos.tar.gz.011
βββ ... (Other task folders have the same structure as above.)
βββ >>> dataset_without_depth/ <<<
βββ clean_the_desktop_part_1/
β βββ data.tar.gz.000
β βββ meta.tar.gz.000
β βββ videos.tar.gz.000
βββ clean_the_desktop_part_2/
β βββ data.tar.gz.000
β βββ meta.tar.gz.000
β βββ videos.tar.gz.000
βββ ... (Other task folders have the same structure as above.)
dataset_without_depth: version without depth data
The directory structure after decompression should be as follows:
{task name}/
βββ meta/
β βββ episodes.jsonl
β βββ episodes_stats.jsonl
β βββ info.json
β βββ tasks.jsonl
βββ data/
β βββ chunk-000/
β βββ episode_000000.parquet
β βββ ...
β βββ episode_000400.parquet
βββ videos/
βββ chunk-000/
βββ observation.images.hand_left/
βββ observation.images.hand_right/
βββ observation.images.top_head/
βββ observation.images.hand_right_depth/
βββ observation.images.hand_left_depth/
βββ observation.images.head_depth/
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