G1edu-u3_place_apple_c

# G1edu-u3_place_apple_c

## 📋 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:
- `pick`
- `place`

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total Episodes** | 29 |
| **Total Frames** | 6567 |
| **Total Tasks** | 1 |
| **Total Videos** | 29 |
| **Total Chunks** | 1 |
| **Chunk Size** | 30 |
| **FPS** | 30 |
| **Dataset Size** | 81.6MB |

## 👥 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 red apple
Pick up the red apple
Pick up the water bottle
Pick up the water bottle
Pick up the white cup
Pick up the lemon
Place the red apple
Place the red apple in the basket
Place the red apple on the blue rectangle plate
Place the red apple on the orange round plate
Place the water bottle
Place the water bottle
Place the white cup
Place the lemon on the blue rectangle plate
Place the lemon on the orange round plate

### Sub-Tasks
This dataset includes 4 distinct subtasks:

1. **End**
2. **null**
3. **Place the apple on the table with left gripper**
4. **Place the apple on the table with right gripper**

## 🎥 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 0:28

## 📁 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 30

### 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": 29, "total_frames": 6567, "total_tasks": 1, "total_videos": 29, "total_chunks": 1, "chunks_size": 30, "fps": 30, "splits": {"train": "0:28"}, "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_place_apple_c_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_place_apple_c

## 📋 概述（Overview）

本数据集采用基于LeRobot的扩展格式，且与LeRobot完全兼容。

**机器人类型（Robot Type）**：`G1edu-u3`
| **代码库版本（Codebase Version）**：`v2.1`
**末端执行器类型（End-Effector Type）**：`three_finger_hand`（三指手）

## 🏠 场景类型（Scene Types）

本数据集涵盖以下场景类型：
- `home`（家居）

## 🤖 原子动作（Atomic Actions）

本数据集包含以下原子动作：
- `pick`（拾取）
- `place`（放置）

## 📊 数据集统计（Dataset Statistics）

| 指标（Metric） | 数值（Value） |
|--------|-------|
| 总集数（Total Episodes） | 29 |
| 总帧数（Total Frames） | 6567 |
| 总任务数（Total Tasks） | 1 |
| 总视频数（Total Videos） | 29 |
| 总块数（Total Chunks） | 1 |
| 块大小（Chunk Size） | 30 |
| 帧率（FPS） | 30 |
| 数据集大小（Dataset Size） | 81.6MB |

## 👥 作者（Authors）

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

## 🔗 链接（Links）

- **🏠 主页（Homepage）**：[https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/)
- **📄 论文（Paper）**：[https://arxiv.org/abs/2511.17441](https://arxiv.org/abs/2511.17441)
- **💻 仓库（Repository）**：[https://github.com/FlagOpen/RoboCOIN](https://github.com/FlagOpen/RoboCOIN)
- **🌐 项目页（Project Page）**：[https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/)
- **🐛 问题反馈（Issues）**：[https://github.com/FlagOpen/RoboCOIN/issues](https://github.com/FlagOpen/RoboCOIN/issues)
- **📜 许可证（License）**：apache-2.0

## 🏷️ 任务描述（Task Descriptions）

### 主要任务（Primary Tasks）
拾取红苹果
拾取红苹果
拾取水瓶
拾取水瓶
拾取白色杯子
拾取柠檬
放置红苹果
将红苹果放置于篮筐中
将红苹果放置于蓝色矩形盘上
将红苹果放置于橙色圆形盘上
放置水瓶
放置水瓶
放置白色杯子
将柠檬放置于蓝色矩形盘上
将柠檬放置于橙色圆形盘上

### 子任务（Sub-Tasks）
本数据集包含4个不同的子任务：

1. **结束（End）**
2. **空（null）**
3. **用左手夹具将苹果放置于桌上**
4. **用右手夹具将苹果放置于桌上**

## 🎥 相机视角（Camera Views）

本数据集包含1个相机视角。

## 🏷️ 可用标注（Available Annotations）

本数据集包含丰富的标注，以支持多样化的学习方法：

### 子任务标注（Subtask Annotations）
- **子任务分割**：细粒度的子任务分割与标注

### 场景标注（Scene Annotations）
- **场景级描述**：语义场景分类与描述

### 末端执行器标注（End-Effector Annotations）
- **方向**：机器人末端执行器的运动方向分类
- **速度**：操作过程中的速度大小分类
- **加速度**：运动分析中的加速度大小分类

### 夹具标注（Gripper Annotations）
- **夹具模式**：夹具控制的开合状态标注
- **夹具活动**：活动状态分类（激活/未激活）

### 附加特征（Additional Features）
- **末端执行器仿真位姿**：仿真空间中末端执行器的6D位姿信息
- 适用于状态与动作
- **夹具开合尺度**：连续的夹具开合测量值
- 适用于状态与动作

## 📂 数据划分（Data Splits）

数据集按以下方式划分：

- **训练集**：第0至28集（Episodes 0:28）

## 📁 数据集结构（Dataset Structure）

本数据集遵循LeRobot格式，包含以下组件：

### 数据文件（Data Files）
- **视频**：包含RGB相机观测的压缩视频文件
- **状态数据**：机器人关节位置、速度及其他状态信息
- **动作数据**：机器人动作指令与轨迹
- **元数据**：剧集元数据、时间戳及标注

### 文件组织（File Organization）
- **数据路径模式**：`data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet`
- **视频路径模式**：`videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4`
- **分块**：数据被组织为1个块，大小为30

### 特征 schema（Features Schema）

数据集包含以下特征：

#### 视觉观测（Visual Observations）
- **observation.images.ego_view**：视频
- 帧率（FPS）：30
- 编码格式（Codec）：h264

#### 状态与动作（State and Action）
- **observation.state**：float32
- **action**：float32

#### 时间信息（Temporal Information）
- **timestamp**：float64
- **frame_index**：int64
- **episode_index**：int64
- **index**：int64
- **task_index**：int64

#### 标注（Annotations）
- **subtask_annotation**：int32
- **scene_annotation**：int32

#### 运动特征（Motion Features）
- **eef_sim_pose_state**：float32
- 维度：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
- 维度：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
- 维度：left_eef_direction、right_eef_direction
- **eef_direction_action**：int32
- 维度：left_eef_direction、right_eef_direction
- **eef_velocity_state**：int32
- 维度：left_eef_velocity、right_eef_velocity
- **eef_velocity_action**：int32
- 维度：left_eef_velocity、right_eef_velocity
- **eef_acc_mag_state**：int32
- 维度：left_eef_acc_mag、right_eef_acc_mag
- **eef_acc_mag_action**：int32
- 维度：left_eef_acc_mag、right_eef_acc_mag

## 📞 联系与支持（Contact and Support）

若对本数据集有疑问、问题或反馈，请联系：
- **邮箱**：无
若对本数据集有疑问、问题或反馈，请联系我们。

### 支持
如需技术支持，请在我们的GitHub仓库提交issue。

## 📄 许可证（License）

本数据集以**apache-2.0**许可证发布。

请参考LICENSE文件获取完整的许可证条款与条件。

## 📚 引用（Citation）

若您在研究中使用本数据集，请引用：

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}
}

### 附加引用

若您使用本数据集，还请考虑引用：
- LeRobot框架：https://github.com/huggingface/lerobot

## 📌 版本信息（Version Information）

## 版本历史（Version History）
- v1.0.0（2025-11）：首次发布