Datasets:

OpenDriveLab-org
/

Kai0

license: cc-by-nc-sa-4.0
task_categories:
  - robotics
tags:
  - LeRobot
configs:
  - config_name: default
    data_files: FlattenFold/base/data/chunk-000/episode_000000.parquet

χ₀ (KAI0)

χ₀ (kai0) is a resource-efficient framework for achieving production-level robustness in robotic manipulation by taming distributional inconsistencies. It enables long-horizon garment manipulation tasks such as flattening, folding, and hanging using dual-arm robots.

TODO

The advantage label will be coming soon.

About the Dataset
Load the Dataset
Download the Dataset
Dataset Structure
- Folder hierarchy
- Details
License and Citation

About the Dataset

~181 hours real world scenarios
Main Tasks
- FlattenFold
  - Single task
  - Initial state: T-shirts are randomly tossed onto the table, presenting random crumpled configurations
  - Manipulation task: Operate the robotic arm to unfold the garment, then fold it
- HangCloth
  - Single task
  - Initial state: Hanger is randomly placed, garment is randomly positioned on the table
  - Manipulation task: Operate the robotic arm to thread the hanger through the garment, then hang it on the rod
- TeeShirtSort
  - Garment classification and arrangement task
  - Initial state: Randomly pick a garment from the laundry basket
  - Classification: Determine whether the garment is a T-shirt or a dress shirt
  - Manipulation task:
    - If it is a T-shirt, fold the garment
    - If it is a dress shirt, expose the collar, then push it to one side of the table

Count of the dataset

Task	Base (episodes count/hours)	DAgger (episodes count/hours)	Total(episodes count/hours)
FlattenFold	3,055/~42 hours	3,457/ ~13 Hours	6,512 /~55 hours
HangCloth	6954/~61 hours	686/~12 hours	7640/~73 hours
TeeShirtSort	5988/~31 hours	769/~22 hours	6757/~53 hours
Total	15,997/~134 hours	4,912/~47 hours	20,909/~181 hours

Load the dataset

This dataset was created using LeRobot
The dataset's version is LeRobotDataset v2.1

For LeRobot version < 0.4.0

Choose the appropriate import based on your version:

Version	Import Path
`<= 0.1.0`	`from lerobot.common.datasets.lerobot_dataset import LeRobotDataset`
`> 0.1.0` and `< 0.4.0`	`from lerobot.datasets.lerobot_dataset import LeRobotDataset`

# For version <= 0.1.0
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

# For version > 0.1.0 and < 0.4.0
from lerobot.datasets.lerobot_dataset import LeRobotDataset

# Load the dataset
dataset = LeRobotDataset(repo_id='OpenDriveLab-org/kai0')

For LeRobot version >= 0.4.0

You need to migrate the dataset from v2.1 to v3.0 first. See the official documentation: Migrate the dataset from v2.1 to v3.0

python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=OpenDriveLab-org/kai0

Download the Dataset

Python Script

from huggingface_hub import hf_hub_download, snapshot_download
from datasets import load_dataset

# Download a single file
hf_hub_download(
    repo_id="OpenDriveLab-org/kai0", 
    filename="episodes.jsonl",
    subfolder="meta",
    repo_type="dataset",
    local_dir="where/you/want/to/save"
)

# Download a specific folder
snapshot_download(
    repo_id="OpenDriveLab-org/kai0", 
    local_dir="/where/you/want/to/save",
    repo_type="dataset",
    allow_patterns=["data/*"]
)

# Load the entire dataset
dataset = load_dataset("OpenDriveLab-org/kai0")

Terminal (CLI)

# Download a single file
hf download OpenDriveLab-org/kai0 \
    --include "meta/info.json" \
    --repo-type dataset \
    --local-dir "/where/you/want/to/save"

# Download a specific folder
hf download OpenDriveLab-org/kai0 \
    --repo-type dataset \
    --include "meta/*" \
    --local-dir "/where/you/want/to/save"

# Download the entire dataset
hf download OpenDriveLab-org/kai0 \
    --repo-type dataset \
    --local-dir "/where/you/want/to/save"

Dataset Structure

Folder hierarchy

Under each task directory, data is partitioned into two subsets: base and dagger.

base contains original demonstration trajectories.
dagger contains on-policy recovery trajectories collected via iterative DAgger.

Kai0-data/
├── FlattenFold/
│   ├── base/
│   │   ├── data/
│   │   ├── videos/
│   │   └── meta/
│   └── dagger/
├── HangCloth/
│   ├── base/
│   └── dagger/
├── TeeShirtSort/
│   ├── base/
│   └── dagger/
└── README.md

Details

info.json

The basic structure of info.json includes metadata about robot types, frames, tasks, and data features like camera perspectives (top_head, hand_left, hand_right).

Parquet file format

Field Name	shape	Meaning
observation.state	[N, 14]	left `[:, :6]`, right `[:, 7:13]`, joint angle left`[:, 6]`, right `[:, 13]` , gripper open range
action	[N, 14]	left `[:, :6]`, right `[:, 7:13]`, joint angle left`[:, 6]`, right `[:, 13]` , gripper open range
timestamp	[N, 1]	Time elapsed since the start of the episode (in seconds)
frame_index	[N, 1]	Index of this frame within the current episode (0-indexed)
episode_index	[N, 1]	Index of the episode this frame belongs to
index	[N, 1]	Global unique index across all frames in the dataset
task_index	[N, 1]	Index identifying the task type being performed

License and Citation

The data and checkpoints are licensed under CC BY-NC-SA 4.0.

@article{sima2026kai0,
  title={$\chi_{0}$: Resource-Aware Robust Manipulation via Taming Distributional Inconsistencies},
  author={Yu, Checheng and Sima, Chonghao and Jiang, Gangcheng and Zhang, Hai and Mai, Haoguang and Li, Hongyang and Wang, Huijie and Chen, Jin and Wu, Kaiyang and Chen, Li and Zhao, Lirui and Shi, Modi and Luo, Ping and Bu, Qingwen and Peng, Shijia and Li, Tianyu and Yuan, Yibo},
  journal={arXiv preprint arXiv:2602.09021},
  year={2026}
}