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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/
β”œβ”€β”€ clean_the_desktop_part_1/
β”‚   β”œβ”€β”€ data.tar.gz.000
β”‚   β”œβ”€β”€ meta.tar.gz.000
β”‚   β”œβ”€β”€ videos.tar.gz.000
β”‚   β”œβ”€β”€ ...
β”‚   └── videos.tar.gz.013
β”œβ”€β”€ clean_the_desktop_part_2/
β”‚   β”œβ”€β”€ data.tar.gz.000
β”‚   β”œβ”€β”€ meta.tar.gz.000
β”‚   β”œβ”€β”€ videos.tar.gz.000
β”‚   β”œβ”€β”€ ...
β”‚   └── videos.tar.gz.011
β”œβ”€β”€ ... (Other task folders have the same structure as above.)
└── >>> dataset_without_depth/ <<<
    β”œβ”€β”€ clean_the_desktop_part_1/
    β”‚   β”œβ”€β”€ data.tar.gz.000
    β”‚   β”œβ”€β”€ meta.tar.gz.000
    β”‚   β”œβ”€β”€ videos.tar.gz.000
    β”œβ”€β”€ clean_the_desktop_part_2/
    β”‚   β”œβ”€β”€ data.tar.gz.000
    β”‚   β”œβ”€β”€ meta.tar.gz.000
    β”‚   β”œβ”€β”€ videos.tar.gz.000
    └── ... (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}/
β”œβ”€β”€ meta/
β”‚   β”œβ”€β”€ episodes.jsonl
β”‚   β”œβ”€β”€ episodes_stats.jsonl
β”‚   β”œβ”€β”€ info.json
β”‚   └── tasks.jsonl
β”œβ”€β”€ data/
β”‚   └── chunk-000/
β”‚       β”œβ”€β”€ episode_000000.parquet
β”‚       β”œβ”€β”€ ...
β”‚       └── episode_000400.parquet
└── videos/
    └── chunk-000/
        β”œβ”€β”€ observation.images.hand_left/
        β”œβ”€β”€ observation.images.hand_right/
        β”œβ”€β”€ observation.images.top_head/
        β”œβ”€β”€ observation.images.hand_right_depth/
        β”œβ”€β”€ observation.images.hand_left_depth/
        └── observation.images.head_depth/
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