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--- |
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license: mit |
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task_categories: |
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- robotics |
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tags: |
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- LeRobot |
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configs: |
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- config_name: default |
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data_files: FlattenFold/base/data/chunk-000/episode_000000.parquet |
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--- |
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# KAI0 |
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<div align="center"> |
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<a href=""> |
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<img src="https://img.shields.io/badge/GitHub-grey?logo=GitHub" alt="GitHub Badge"> |
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</a> |
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<a href="https://mmlab.hk/research/kai0"> |
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<img src="https://img.shields.io/badge/Research_Blog-grey?style=flat" alt="Research Blog Badge"> |
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</a> |
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</div> |
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# TODO |
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- [ ] The advantage label will be coming soon. |
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## Contents |
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- [About the Dataset](#about-the-dataset) |
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- [Load the Dataset](#get-started) |
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- [Download the Dataset](#download-the-dataset) |
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- [Dataset Structure](#dataset-structure) |
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- [Folder hierarchy](#folder-hierarchy) |
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- [Details](#details) |
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- [License and Citation](#license-and-citation) |
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## [About the Dataset](#contents) |
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- **~134 hours** real world scenarios |
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- **Main Tasks** |
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- ***FlattenFold*** |
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- Single task |
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- Initial state: T-shirts are randomly tossed onto the table, presenting random crumpled configurations |
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- Manipulation task: Operate the robotic arm to unfold the garment, then fold it |
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- ***HangCloth*** |
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- Single task |
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- Initial state: Hanger is randomly placed, garment is randomly positioned on the table |
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- Manipulation task: Operate the robotic arm to thread the hanger through the garment, then hang it on the rod |
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- ***TeeShirtSort*** |
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- Garment classification and arrangement task |
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- Initial state: Randomly pick a garment from the laundry basket |
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- Classification: Determine whether the garment is a T-shirt or a dress shirt |
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- Manipulation task: |
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- If it is a T-shirt, fold the garment |
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- If it is a dress shirt, expose the collar, then push it to one side of the table |
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- **Count of the dataset** |
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| Task | Base (episodes count/hours) | DAgger (episodes count/hours) | Total(episodes count/hours) | |
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|------|-----------------------------|-------------------------------|-----------------------------| |
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| FlattenFold | 3,055/~42 hours | 3,457/ ~13 Hours | 6,512 /~55 hours | |
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| HangCloth | 6954/~61 hours | 686/~12 hours | 7640/~73 hours | |
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| TeeShirtSort | 5988/~31 hours | 769/~22 hours | 6757/~53 hours | |
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| **Total** | **15,997/~134 hours** | **4,912/~47 hours** | **20,909/~181 hours** | |
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## [Load the dataset](#contents) |
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- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot) |
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- The dataset's version is LeRobotDataset v2.1 |
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### For LeRobot version < 0.4.0 |
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Choose the appropriate import based on your version: |
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| Version | Import Path | |
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|------------------------|-------------| |
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| `<= 0.1.0` | `from lerobot.common.datasets.lerobot_dataset import LeRobotDataset` | |
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| `> 0.1.0` and `< 0.4.0` | `from lerobot.datasets.lerobot_dataset import LeRobotDataset` | |
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```python |
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# For version <= 0.1.0 |
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from lerobot.common.datasets.lerobot_dataset import LeRobotDataset |
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# For version > 0.1.0 and < 0.4.0 |
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from lerobot.datasets.lerobot_dataset import LeRobotDataset |
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# Load the dataset |
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dataset = LeRobotDataset(repo_id='where/the/dataset/you/stored') |
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``` |
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### For LeRobot version >= 0.4.0 |
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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](https://huggingface.co/docs/lerobot/lerobot-dataset-v3) |
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```bash |
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python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DATASET_ID> |
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``` |
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## [Download the Dataset](#contents) |
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### Python Script |
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```python |
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from huggingface_hub import hf_hub_download, snapshot_download |
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from datasets import load_dataset |
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# Download a single file |
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hf_hub_download( |
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repo_id="OpenDriveLab-org/kai0", |
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filename="episodes.jsonl", |
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subfolder="meta", |
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repo_type="dataset", |
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local_dir="where/you/want/to/save" |
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) |
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# Download a specific folder |
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snapshot_download( |
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repo_id="OpenDriveLab-org/kai0", |
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local_dir="/where/you/want/to/save", |
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repo_type="dataset", |
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allow_patterns=["data/*"] |
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) |
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# Load the entire dataset |
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dataset = load_dataset("OpenDriveLab-org/kai0") |
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``` |
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### Terminal (CLI) |
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```bash |
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# Download a single file |
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hf download OpenDriveLab-org/kai0 \ |
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--include "meta/info.json" \ |
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--repo-type dataset \ |
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--local-dir "/where/you/want/to/save" |
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# Download a specific folder |
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hf download OpenDriveLab-org/kai0 \ |
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--repo-type dataset \ |
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--include "meta/*" \ |
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--local-dir "/where/you/want/to/save" |
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# Download the entire dataset |
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hf download OpenDriveLab-org/kai0 \ |
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--repo-type dataset \ |
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--local-dir "/where/you/want/to/save" |
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``` |
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## [Dataset Structure](#contents) |
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### [Folder hierarchy](#contents) |
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Under each task directory, data is partitioned into two subsets: base and dagger. |
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- base |
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contains |
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original demonstration trajectories of robotic arm manipulation for garment arrangement tasks. |
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- dagger |
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contains on-policy recovery trajectories collected via iterative DAgger, designed to populate failure recovery modes absent in static demonstrations. |
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```text |
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Kai0-data/ |
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├── FlattenFold/ |
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│ ├── base/ |
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│ │ ├── data/ |
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│ │ │ ├── chunk-000/ |
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│ │ │ │ ├── episode_000000.parquet |
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│ │ │ │ ├── episode_000001.parquet |
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│ │ │ │ └── ... |
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│ │ │ └── ... |
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│ │ ├── videos/ |
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│ │ │ ├── chunk-000/ |
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│ │ │ │ ├── observation.images.hand_left/ |
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│ │ │ │ │ ├── episode_000000.mp4 |
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│ │ │ │ │ ├── episode_000001.mp4 |
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│ │ │ │ │ └── ... |
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│ │ │ │ ├── observation.images.hand_right/ |
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│ │ │ │ │ ├── episode_000000.mp4 |
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│ │ │ │ │ ├── episode_000001.mp4 |
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│ │ │ │ │ └── ... |
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│ │ │ │ ├── observation.images.top_head/ |
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│ │ │ │ │ ├── episode_000000.mp4 |
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│ │ │ │ │ ├── episode_000001.mp4 |
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│ │ │ │ │ └── ... |
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│ │ │ │ └── ... |
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│ │ │ └── ... |
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│ │ └── meta/ |
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│ │ ├── info.json |
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│ │ ├── episodes.jsonl |
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│ │ ├── tasks.jsonl |
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│ │ └── episodes_stats.jsonl |
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│ └── dagger/ |
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├── HangCloth/ |
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│ ├── base/ |
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│ └── dagger/ |
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├── TeeShirtSort/ |
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│ ├── base/ |
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│ └── dagger/ |
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└── README.md |
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``` |
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<a id='Details'></a> |
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### [Details](#contents) |
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#### info.json |
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the basic struct of the [info.json](#meta/info.json) |
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```json |
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{ |
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"codebase_version": "v2.1", |
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"robot_type": "agilex", |
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"total_episodes": ..., # the total episodes in the dataset |
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"total_frames": ..., # The total number of video frames in any single camera perspective |
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"total_tasks": ..., # Total number of tasks |
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"total_videos": ..., # The total number of videos from all camera perspectives in the dataset |
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"total_chunks": ..., # The number of chunks in the dataset |
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"chunks_size": ..., # The max number of episodes in a chunk |
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"fps": ..., # Video frame rate per second |
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"splits": { # how to split the dataset |
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"train": ... |
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}, |
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"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", |
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"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", |
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"features": { |
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"observation.images.top_head": { # the camera perspective |
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"dtype": "video", |
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"shape": [ |
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480, |
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640, |
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3 |
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], |
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"names": [ |
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"height", |
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"width", |
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"channel" |
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], |
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"info": { |
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"video.height": 480, |
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"video.width": 640, |
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"video.codec": "av1", |
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"video.pix_fmt": "yuv420p", |
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"video.is_depth_map": false, |
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"video.fps": 30, |
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"video.channels": 3, |
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"has_audio": false |
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} |
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}, |
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"observation.images.hand_left": { # the camera perspective |
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... |
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}, |
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"observation.images.hand_right": { # the camera perspective |
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... |
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}, |
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"observation.state": { |
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"dtype": "float32", |
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"shape": [ |
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14 |
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], |
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"names": null |
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}, |
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"action": { |
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"dtype": "float32", |
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"shape": [ |
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14 |
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], |
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"names": null |
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}, |
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"timestamp": { |
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"dtype": "float32", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"frame_index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"episode_index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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"names": null |
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}, |
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"task_index": { |
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"dtype": "int64", |
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"shape": [ |
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1 |
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], |
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"names": null |
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} |
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} |
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} |
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``` |
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#### [Parquet file format](#contents) |
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| Field Name | shape | Meaning | |
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|------------|-------------|-------------| |
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| observation.state | [N, 14] |left `[:, :6]`, right `[:, 7:13]`, joint angle<br> left`[:, 6]`, right `[:, 13]` , gripper open range| |
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| action | [N, 14] |left `[:, :6]`, right `[:, 7:13]`, joint angle<br>left`[:, 6]`, right `[:, 13]` , gripper open range | |
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| timestamp | [N, 1] | Time elapsed since the start of the episode (in seconds) | |
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| frame_index | [N, 1] | Index of this frame within the current episode (0-indexed) | |
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| episode_index | [N, 1] | Index of the episode this frame belongs to | |
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| index | [N, 1] | Global unique index across all frames in the dataset | |
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| task_index | [N, 1] | Index identifying the task type being performed | |
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### [tasks.jsonl](#FlattenFold/meta/tasks.jsonl) |
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Contains task language prompts (natural language instructions) that specify the manipulation task to be performed. Each entry maps a task_index to its corresponding task description, which can be used for language-conditioned policy training. |
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# License and Citation |
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All the data and code within this repo are under [](). Please consider citing our project if it helps your research. |
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```BibTeX |
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@misc{, |
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title={}, |
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author={}, |
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howpublished={\url{}}, |
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year={} |
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} |
