--- license: cc-by-nc-nd-4.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet language: - en --- ⚠️ !!! 等待信息,填充链接
GitHub Badge Project Page Badge Research Blog Badge Research Blog Badge
# Contents - [About the Dataset](#about-the-dataset) - [Dataset Structure](#dataset-structure) - [Folder hierarchy](#folder-hierarchy) - [Details](#details) - [Download the Dataset](#download-the-dataset) - [Load the Dataset](#get-started) - [License and Citation](#license-and-citation) # [About the Dataset](#contents) - This dataset was created using [LeRobot](https://github.com/huggingface/lerobot) - **~200 hours real world scenarios** across **1** main task, **3** sub tasks - A clothing organization task that involves identifying the type of clothing and determining the next action based on its category - **sub-tasks** - **Folding** - Randomly pick a piece of clothing from the basket and place it on the workbench - If it is a short T-shirt, fold it - **Hanging Preparation** - Randomly pick a piece of clothing from the basket and place it on the workbench - If it is a dress shirt, locate the collar and drag the clothing to the right side - **Hanging** - Hang the dress shirt properly # [Dataset Structure](#contents) ## [Folder hierarchy](#contents) ```text dataset_root/ ├── data/ │ ├── chunk-000/ │ │ ├── episode_000000.parquet │ │ ├── episode_000001.parquet │ │ └── ... │ └── ... ├── videos/ │ ├── chunk-000/ │ │ ├── observation.images.hand_left │ │ │ ├── episode_000000.mp4 │ │ │ ├── episode_000001.mp4 │ │ │ └── ... │ │ ├── observation.images.hand_right │ │ │ ├── episode_000000.mp4 │ │ │ ├── episode_000001.mp4 │ │ │ └── ... │ │ ├── observation.images.top_head │ │ │ ├── episode_000000.mp4 │ │ │ ├── episode_000001.mp4 │ │ │ └── ... │ │ └── ... ├── meta/ │ ├── info.json │ ├── episodes.jsonl │ ├── tasks.jsonl │ └── episodes_stats.jsonl └ README.md ``` ## [Details](#contents) ### info.json the basic struct of the [info.json](#meta/info.json) ```json { "codebase_version": "v2.1", "robot_type": "agilex", "total_episodes": ..., "total_frames": ..., "total_tasks": ..., "total_videos": ..., "total_chunks": ..., "chunks_size": ..., "fps": ..., "splits": { "train": ... }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.images.top_head": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.hand_left": { ... }, "observation.images.hand_right": { ... }, "observation.state": { "dtype": "float32", "shape": [ 14 ], "names": null }, "action": { "dtype": "float32", "shape": [ 14 ], "names": null }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } ``` ### [Parquet file format](#contents) | 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 | ## [tasks.jsonl](#meta/tasks.jsonl) positive/negitive: Labels indicating the advantage of each frame's action, where "positive" means the action benefits future outcomes and "negative" means otherwise. # [Download the Dataset](#contents) ### Python Script ```python 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) ```bash # 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" ``` # [Load the dataset](#contents) ## 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` | ```python # 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='where/the/dataset/you/stored') ``` ## 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](https://huggingface.co/docs/lerobot/lerobot-dataset-v3) ```bash python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id= ``` ⚠️ !!! 等待信息填充 # License and Citation All the data and code within this repo are under [](). Please consider citing our project if it helps your research. ```BibTeX @misc{, title={}, author={}, howpublished={\url{}}, year={} }