G1edu-u3_pick_up_the_bread_az

# G1edu-u3_pick_up_the_bread_az

## 📋 Overview

This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.

**Robot Type:** `G1edu-u3`
| **Codebase Version:** `v2.1`
**End-Effector Type:** `three_finger_hand`

## 🏠 Scene Types

This dataset covers the following scene types:
- `home`

## 🤖 Atomic Actions

This dataset includes the following atomic actions:
- `grasp`
- `pick`

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total Episodes** | 26 |
| **Total Frames** | 14542 |
| **Total Tasks** | 1 |
| **Total Videos** | 26 |
| **Total Chunks** | 1 |
| **Chunk Size** | 27 |
| **FPS** | 30 |
| **Dataset Size** | 169.7MB |

## 👥 Authors

### Contributors
This dataset is contributed by:
- [RoboCOIN](https://flagopen.github.io/RoboCOIN/) - RoboCOIN Team

## 🔗 Links

- **🏠 Homepage:** [https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/)
- **📄 Paper:** [https://arxiv.org/abs/2511.17441](https://arxiv.org/abs/2511.17441)
- **💻 Repository:** [https://github.com/FlagOpen/RoboCOIN](https://github.com/FlagOpen/RoboCOIN)
- **🌐 Project Page:** [https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/)
- **🐛 Issues:** [https://github.com/FlagOpen/RoboCOIN/issues](https://github.com/FlagOpen/RoboCOIN/issues)
- **📜 License:** apache-2.0

## 🏷️ Dataset Tags

- `RoboCOIN`
- `LeRobot`

## 🎯 Task Descriptions

### Primary Tasks
Pick up the water bottle
Pick up the bread
Pick up the pink cup
Pick up the lemmon
Pick up the tissue box
Pick up the toy bear
Place the water bottle
Place the bread
Place the pink cup
Place the lemon
Place the lemon on the orange round plate
Place the tissue box
Place the toy bear

### Sub-Tasks
This dataset includes 5 distinct subtasks:

1. **Abnormal**
2. **End**
3. **Grasp the long bread and lift it to the center of the view with left gripper**
4. **Grasp the long bread and lift it to the center of the view with right gripper**
5. **null**

## 🎥 Camera Views

This dataset includes 1 camera views.

## 🏷️ Available Annotations

This dataset includes rich annotations to support diverse learning approaches:

### Subtask Annotations
- **Subtask Segmentation**: Fine-grained subtask segmentation and labeling
### Scene Annotations
- **Scene-level Descriptions**: Semantic scene classifications and descriptions
### End-Effector Annotations
- **Direction**: Movement direction classifications for robot end-effectors
- **Velocity**: Velocity magnitude categorizations during manipulation
- **Acceleration**: Acceleration magnitude classifications for motion analysis

### Gripper Annotations
- **Gripper Mode**: Open/close state annotations for gripper control
- **Gripper Activity**: Activity state classifications (active/inactive)

### Additional Features
- **End-Effector Simulation Pose**: 6D pose information for end-effectors in simulation space
- Available for both state and action
- **Gripper Opening Scale**: Continuous gripper opening measurements
- Available for both state and action

## 📂 Data Splits

The dataset is organized into the following splits:

- **Training**: Episodes 0:25

## 📁 Dataset Structure

This dataset follows the LeRobot format and contains the following components:

### Data Files
- **Videos**: Compressed video files containing RGB camera observations
- **State Data**: Robot joint positions, velocities, and other state information
- **Action Data**: Robot action commands and trajectories
- **Metadata**: Episode metadata, timestamps, and annotations

### File Organization
- **Data Path Pattern**: `data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet`
- **Video Path Pattern**: `videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4`
- **Chunking**: Data is organized into 1 chunk(s)
of size 27

### Features Schema

The dataset includes the following features:

#### Visual Observations
- **observation.images.ego_view**: video
- FPS: 30
- Codec: h264

#### State and Action- **observation.state**: float32- **action**: float32

#### Temporal Information
- **timestamp**: float64
- **frame_index**: int64
- **episode_index**: int64
- **index**: int64
- **task_index**: int64

#### Annotations
- **subtask_annotation**: int32
- **scene_annotation**: int32

#### Motion Features
- **eef_sim_pose_state**: float32
- Dimensions: left_eef_pos_x, left_eef_pos_y, left_eef_pos_z, left_eef_ori_x, left_eef_ori_y, left_eef_ori_z, right_eef_pos_x, right_eef_pos_y, right_eef_pos_z, right_eef_ori_x, right_eef_ori_y, right_eef_ori_z
- **eef_sim_pose_action**: float32
- Dimensions: left_eef_pos_x, left_eef_pos_y, left_eef_pos_z, left_eef_ori_x, left_eef_ori_y, left_eef_ori_z, right_eef_pos_x, right_eef_pos_y, right_eef_pos_z, right_eef_ori_x, right_eef_ori_y, right_eef_ori_z
- **eef_direction_state**: int32
- Dimensions: left_eef_direction, right_eef_direction
- **eef_direction_action**: int32
- Dimensions: left_eef_direction, right_eef_direction
- **eef_velocity_state**: int32
- Dimensions: left_eef_velocity, right_eef_velocity
- **eef_velocity_action**: int32
- Dimensions: left_eef_velocity, right_eef_velocity
- **eef_acc_mag_state**: int32
- Dimensions: left_eef_acc_mag, right_eef_acc_mag
- **eef_acc_mag_action**: int32
- Dimensions: left_eef_acc_mag, right_eef_acc_mag

#### Gripper Features

### Meta Information

The complete dataset metadata is available in [meta/info.json](meta/info.json):

```json
{"codebase_version": "v2.1", "robot_type": null, "total_episodes": 26, "total_frames": 14542, "total_tasks": 1, "total_videos": 26, "total_chunks": 1, "chunks_size": 27, "fps": 30, "splits": {"train": "0:25"}, "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.ego_view": {"dtype": "video", "shape": [480, 640, 3], "names": ["height", "width", "channel"], "info": {"video.height": 480, "video.width": 640, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false}}, "observation.state": {"dtype": "float32", "shape": [30], "names": ["left_arm_joint_1_rad", "left_arm_joint_2_rad", "left_arm_joint_3_rad", "left_arm_joint_4_rad", "left_arm_joint_5_rad", "left_arm_joint_6_rad", "left_arm_joint_7_rad", "right_arm_joint_1_rad", "right_arm_joint_2_rad", "right_arm_joint_3_rad", "right_arm_joint_4_rad", "right_arm_joint_5_rad", "right_arm_joint_6_rad", "right_arm_joint_7_rad", "left_hand_joint_1_rad", "left_hand_joint_2_rad", "left_hand_joint_3_rad", "left_hand_joint_4_rad", "left_hand_joint_5_rad", "left_hand_joint_6_rad", "left_hand_joint_7_rad", "right_hand_joint_1_rad", "right_hand_joint_2_rad", "right_hand_joint_3_rad", "right_hand_joint_4_rad", "right_hand_joint_5_rad", "right_hand_joint_6_rad", "right_hand_joint_7_rad"]}, "action": {"dtype": "float32", "shape": [30], "names": ["left_arm_joint_1_rad", "left_arm_joint_2_rad", "left_arm_joint_3_rad", "left_arm_joint_4_rad", "left_arm_joint_5_rad", "left_arm_joint_6_rad", "left_arm_joint_7_rad", "right_arm_joint_1_rad", "right_arm_joint_2_rad", "right_arm_joint_3_rad", "right_arm_joint_4_rad", "right_arm_joint_5_rad", "right_arm_joint_6_rad", "right_arm_joint_7_rad", "left_hand_joint_1_rad", "left_hand_joint_2_rad", "left_hand_joint_3_rad", "left_hand_joint_4_rad", "left_hand_joint_5_rad", "left_hand_joint_6_rad", "left_hand_joint_7_rad", "right_hand_joint_1_rad", "right_hand_joint_2_rad", "right_hand_joint_3_rad", "right_hand_joint_4_rad", "right_hand_joint_5_rad", "right_hand_joint_6_rad", "right_hand_joint_7_rad"]}, "timestamp": {"dtype": "float64", "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}, "subtask_annotation": {"names": null, "dtype": "int32", "shape": [5]}, "scene_annotation": {"names": null, "dtype": "int32", "shape": [1]}, "eef_sim_pose_state": {"names": ["left_eef_pos_x", "left_eef_pos_y", "left_eef_pos_z", "left_eef_ori_x", "left_eef_ori_y", "left_eef_ori_z", "right_eef_pos_x", "right_eef_pos_y", "right_eef_pos_z", "right_eef_ori_x", "right_eef_ori_y", "right_eef_ori_z"], "dtype": "float32", "shape": [12]}, "eef_sim_pose_action": {"names": ["left_eef_pos_x", "left_eef_pos_y", "left_eef_pos_z", "left_eef_ori_x", "left_eef_ori_y", "left_eef_ori_z", "right_eef_pos_x", "right_eef_pos_y", "right_eef_pos_z", "right_eef_ori_x", "right_eef_ori_y", "right_eef_ori_z"], "dtype": "float32", "shape": [12]}, "eef_direction_state": {"names": ["left_eef_direction", "right_eef_direction"], "dtype": "int32", "shape": [2]}, "eef_direction_action": {"names": ["left_eef_direction", "right_eef_direction"], "dtype": "int32", "shape": [2]}, "eef_velocity_state": {"names": ["left_eef_velocity", "right_eef_velocity"], "dtype": "int32", "shape": [2]}, "eef_velocity_action": {"names": ["left_eef_velocity", "right_eef_velocity"], "dtype": "int32", "shape": [2]}, "eef_acc_mag_state": {"names": ["left_eef_acc_mag", "right_eef_acc_mag"], "dtype": "int32", "shape": [2]}, "eef_acc_mag_action": {"names": ["left_eef_acc_mag", "right_eef_acc_mag"], "dtype": "int32", "shape": [2]}}, "discarded_episode_indices": []}
```

### Directory Structure

The dataset is organized as follows (showing leaf directories with first 5 files only):

```
G1edu-u3_pick_up_the_bread_az_qced_hardlink/
├── annotations/
│ ├── eef_acc_mag_annotation.jsonl
│ ├── eef_direction_annotation.jsonl
│ ├── eef_velocity_annotation.jsonl
│ ├── gripper_activity_annotation.jsonl
│ ├── gripper_mode_annotation.jsonl
│ └── (...)
├── data/
│ └── chunk-000/
│ ├── episode_000000.parquet
│ ├── episode_000001.parquet
│ ├── episode_000002.parquet
│ ├── episode_000003.parquet
│ ├── episode_000004.parquet
│ └── (...)
├── meta/
│ ├── episodes.jsonl
│ ├── episodes_stats.jsonl
│ ├── info.json
│ └── tasks.jsonl
└── videos/
└── chunk-000/
└── observation.images.ego_view/
├── episode_000000.mp4
├── episode_000001.mp4
├── episode_000002.mp4
├── episode_000003.mp4
├── episode_000004.mp4
└── (...)
```

## 📞 Contact and Support

For questions, issues, or feedback regarding this dataset, please contact:
- **Email:** None
For questions, issues, or feedback regarding this dataset, please contact us.
### Support
For technical support, please open an issue on our GitHub repository.

## 📄 License

This dataset is released under the **apache-2.0** license.

Please refer to the LICENSE file for full license terms and conditions.

## 📚 Citation

If you use this dataset in your research, please cite:

```bibtex
@article{robocoin,
title={RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation},
author={Shihan Wu, Xuecheng Liu, Shaoxuan Xie, Pengwei Wang, Xinghang Li, Bowen Yang, Zhe Li, Kai Zhu, Hongyu Wu, Yiheng Liu, Zhaoye Long, Yue Wang, Chong Liu, Dihan Wang, Ziqiang Ni, Xiang Yang, You Liu, Ruoxuan Feng, Runtian Xu, Lei Zhang, Denghang Huang, Chenghao Jin, Anlan Yin, Xinlong Wang, Zhenguo Sun, Junkai Zhao, Mengfei Du, Mingyu Cao, Xiansheng Chen, Hongyang Cheng, Xiaojie Zhang, Yankai Fu, Ning Chen, Cheng Chi, Sixiang Chen, Huaihai Lyu, Xiaoshuai Hao, Yequan Wang, Bo Lei, Dong Liu, Xi Yang, Yance Jiao, Tengfei Pan, Yunyan Zhang, Songjing Wang, Ziqian Zhang, Xu Liu, Ji Zhang, Caowei Meng, Zhizheng Zhang, Jiyang Gao, Song Wang, Xiaokun Leng, Zhiqiang Xie, Zhenzhen Zhou, Peng Huang, Wu Yang, Yandong Guo, Yichao Zhu, Suibing Zheng, Hao Cheng, Xinmin Ding, Yang Yue, Huanqian Wang, Chi Chen, Jingrui Pang, YuXi Qian, Haoran Geng, Lianli Gao, Haiyuan Li, Bin Fang, Gao Huang, Yaodong Yang, Hao Dong, He Wang, Hang Zhao, Yadong Mu, Di Hu, Hao Zhao, Tiejun Huang, Shanghang Zhang, Yonghua Lin, Zhongyuan Wang and Guocai Yao},
journal={arXiv preprint arXiv:2511.17441},
url = {https://arxiv.org/abs/2511.17441},
year={2025}
}
```

### Additional References

If you use this dataset, please also consider citing:
- LeRobot Framework: https://github.com/huggingface/lerobot

## 📌 Version Information

## Version History
- v1.0.0 (2025-11): Initial release

# G1edu-u3_pick_up_the_bread_az

## 📋 概述
本数据集采用基于LeRobot的扩展格式，与LeRobot框架完全兼容。
**机器人类型**：`G1edu-u3`
**代码库版本**：`v2.1`
**末端执行器类型**：`三指手（three_finger_hand）`

## 🏠 场景类别
本数据集覆盖以下场景类型：
- `home`

## 🤖 原子动作
本数据集包含以下原子动作：
- `抓取（grasp）`
- `拾取（pick）`

## 📊 数据集统计
| 指标 | 数值 |
|--------|-------|
| 总回合数 | 26 |
| 总帧数 | 14542 |
| 总任务数 | 1 |
| 总视频数 | 26 |
| 总数据块数 | 1 |
| 数据块大小 | 27 |
| 帧率（FPS） | 30 |
| 数据集总大小 | 169.7MB |

## 👥 作者
### 贡献者
本数据集由以下团队贡献：
- [RoboCOIN](https://flagopen.github.io/RoboCOIN/) - RoboCOIN团队

## 🔗 相关链接
- 项目主页：[https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/)
- 学术论文：[https://arxiv.org/abs/2511.17441](https://arxiv.org/abs/2511.17441)
- 代码仓库：[https://github.com/FlagOpen/RoboCOIN](https://github.com/FlagOpen/RoboCOIN)
- 问题反馈：[https://github.com/FlagOpen/RoboCOIN/issues](https://github.com/FlagOpen/RoboCOIN/issues)
- 开源协议：Apache-2.0

## 🏷️ 数据集标签
- `RoboCOIN`
- `LeRobot`

## 🎯 任务描述
### 主任务
1. 拾取水瓶
2. 拾取面包
3. 拾取粉色杯子
4. 拾取柠檬
5. 拾取纸巾盒
6. 拾取玩具熊
7. 放置水瓶
8. 放置面包
9. 放置粉色杯子
10. 放置柠檬
11. 将柠檬放置在橙色圆形餐盘上
12. 放置纸巾盒
13. 放置玩具熊

### 子任务
本数据集包含5种不同子任务：
1. **异常（Abnormal）**
2. **结束（End）**
3. 使用左夹持器抓取长条形面包并将其提升至视野中心
4. 使用右夹持器抓取长条形面包并将其提升至视野中心
5. **空（null）**

## 🎥 相机视角
本数据集包含1个相机视角。

## 🏷️ 可用标注
本数据集包含丰富标注以支持多样化学习方法：
### 子任务标注
- 子任务分割：细粒度子任务分割与标注
### 场景标注
- 场景级描述：语义化场景分类与描述
### 末端执行器标注
- 运动方向：机器人末端执行器运动方向分类
- 运动速度：操作过程中的速度量级分类
- 运动加速度：用于运动分析的加速度量级分类
### 夹持器标注
- 夹持器模式：夹持器开合状态标注
- 夹持器活动状态：活动/非活动状态分类
### 附加特性
- 末端执行器仿真位姿：仿真空间中末端执行器6D位姿信息，支持状态与动作数据
- 夹持器开合尺度：连续夹持器开合度测量值，支持状态与动作数据

## 📂 数据划分
本数据集按以下方式组织：
- 训练集：回合0:25

## 📁 数据集结构
本数据集遵循LeRobot格式，目录结构如下：

G1edu-u3_pick_up_the_bread_az_qced_hardlink/
├── annotations/
│ ├── eef_acc_mag_annotation.jsonl
│ ├── eef_direction_annotation.jsonl
│ ├── eef_velocity_annotation.jsonl
│ ├── gripper_activity_annotation.jsonl
│ ├── gripper_mode_annotation.jsonl
│ └── (...)
├── data/
│ └── chunk-000/
│ ├── episode_000000.parquet
│ ├── episode_000001.parquet
│ ├── episode_000002.parquet
│ ├── episode_000003.parquet
│ ├── episode_000004.parquet
│ └── (...)
├── meta/
│ ├── episodes.jsonl
│ ├── episodes_stats.jsonl
│ ├── info.json
│ └── tasks.jsonl
└── videos/
└── chunk-000/
└── observation.images.ego_view/
├── episode_000000.mp4
├── episode_000001.mp4
├── episode_000002.mp4
├── episode_000003.mp4
├── episode_000004.mp4
└── (...)

### 特征Schema
本数据集包含以下特征：
#### 视觉观测
- `observation.images.ego_view`：视频，帧率30，编码格式h264
#### 状态与动作
- `observation.state`：float32类型
- `action`：float32类型
#### 时间信息
- `timestamp`：float64类型
- `frame_index`：int64类型
- `episode_index`：int64类型
- `index`：int64类型
- `task_index`：int64类型
#### 标注信息
- `subtask_annotation`：int32类型
- `scene_annotation`：int32类型
#### 运动特征
- `eef_sim_pose_state`：float32类型，维度包含左右末端执行器位置与姿态
- `eef_sim_pose_action`：float32类型，维度包含左右末端执行器位置与姿态
- `eef_direction_state`：int32类型，维度包含左右末端执行器运动方向
- `eef_direction_action`：int32类型，维度包含左右末端执行器运动方向
- `eef_velocity_state`：int32类型，维度包含左右末端执行器运动速度
- `eef_velocity_action`：int32类型，维度包含左右末端执行器运动速度
- `eef_acc_mag_state`：int32类型，维度包含左右末端执行器加速度量级
- `eef_acc_mag_action`：int32类型，维度包含左右末端执行器加速度量级

### 元数据
完整元数据参见`meta/info.json`，内容如下：
json
{"codebase_version": "v2.1", "robot_type": null, "total_episodes": 26, "total_frames": 14542, "total_tasks": 1, "total_videos": 26, "total_chunks": 1, "chunks_size": 27, "fps": 30, "splits": {"train": "0:25"}, "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.ego_view": {"dtype": "video", "shape": [480, 640, 3], "info": {"video.height": 480, "video.width": 640, "video.codec": "h264", "video.fps": 30}}, "observation.state": {"dtype": "float32", "shape": [30]}, "action": {"dtype": "float32", "shape": [30]}, "timestamp": {"dtype": "float64"}, "frame_index": {"dtype": "int64"}, "episode_index": {"dtype": "int64"}, "index": {"dtype": "int64"}, "task_index": {"dtype": "int64"}, "subtask_annotation": {"dtype": "int32"}, "scene_annotation": {"dtype": "int32"}, "eef_sim_pose_state": {"dtype": "float32", "shape": [12]}, "eef_sim_pose_action": {"dtype": "float32", "shape": [12]}, "eef_direction_state": {"dtype": "int32", "shape": [2]}, "eef_direction_action": {"dtype": "int32", "shape": [2]}, "eef_velocity_state": {"dtype": "int32", "shape": [2]}, "eef_velocity_action": {"dtype": "int32", "shape": [2]}, "eef_acc_mag_state": {"dtype": "int32", "shape": [2]}, "eef_acc_mag_action": {"dtype": "int32", "shape": [2]}}, "discarded_episode_indices": []}

## 📞 联系与支持
若您有任何疑问、问题或反馈，请通过以下方式联系：
- 邮箱：无
- 技术支持：在GitHub仓库提交issue

## 📄 开源协议
本数据集采用Apache-2.0协议发布，详细条款请参见LICENSE文件。

## 📌 版本信息
- v1.0.0 (2025-11)：初始发布

## 📚 引用说明
若您在研究中使用本数据集，请引用以下文献：
bibtex
@article{robocoin,
title={RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation},
author={Shihan Wu, Xuecheng Liu, Shaoxuan Xie, Pengwei Wang, Xinghang Li, Bowen Yang, Zhe Li, Kai Zhu, Hongyu Wu, Yiheng Liu, Zhaoye Long, Yue Wang, Chong Liu, Dihan Wang, Ziqiang Ni, Xiang Yang, You Liu, Ruoxuan Feng, Runtian Xu, Lei Zhang, Denghang Huang, Chenghao Jin, Anlan Yin, Xinlong Wang, Zhenguo Sun, Junkai Zhao, Mengfei Du, Mingyu Cao, Xiansheng Chen, Hongyang Cheng, Xiaojie Zhang, Yankai Fu, Ning Chen, Cheng Chi, Sixiang Chen, Huaihai Lyu, Xiaoshuai Hao, Yequan Wang, Bo Lei, Dong Liu, Yonghua Lin, Zhang, Heng, Zhao, Tiejun Huang, Shanghang Zhang, Zhongyuan Wang, Guocai Yao},
journal={arXiv preprint arXiv:2511.17441},
url={https://arxiv.org/abs/2511.17441},
year={2025}
}

同时请引用LeRobot框架：[https://github.com/huggingface/lerobot](https://github.com/huggingface/lerobot)