Cobot_Magic_move_the_ball_and_the_cube_block

# Cobot_Magic_move_the_ball_and_the_cube_block

## 📋 Overview

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

**Robot Type:** `Cobot_Magic`
| **Codebase Version:** `v2.1`
**End-Effector Type:** `two_finger_gripper`

## 🏠 Scene Types

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

## 🤖 Atomic Actions

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

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total Episodes** | 98 |
| **Total Frames** | 23991 |
| **Total Tasks** | 1 |
| **Total Videos** | 294 |
| **Total Chunks** | 1 |
| **Chunk Size** | 1000 |
| **FPS** | 30 |
| **Dataset Size** | 451.4MB |

## 👥 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 ball and place it in the circular groove above the cube.

### Sub-Tasks
This dataset includes 9 distinct subtasks:

1. **End**
2. **Grasp the sphere on the table**
3. **null**
4. **Pick up the ball**
5. **Pick up the sphere recess**
6. **Place the ball into the sphere recess**
7. **Place the ball on the table**
8. **Place the sphere into the sphere-shaped recess**
9. **Place the sphere recess in the center of the table**

## 🎥 Camera Views

This dataset includes 3 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 0:97

## 📁 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 1000

### Features Schema

The dataset includes the following features:

#### Visual Observations
- **observation.images.cam_high_rgb**: video
- FPS: 30
- Codec: av1- **observation.images.cam_left_wrist_rgb**: video
- FPS: 30
- Codec: av1- **observation.images.cam_right_wrist_rgb**: video
- FPS: 30
- Codec: av1

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

#### Temporal Information
- **timestamp**: float32
- **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
- **gripper_open_scale_state**: float32
- Dimensions: left_gripper_open_scale, right_gripper_open_scale
- **gripper_open_scale_action**: float32
- Dimensions: left_gripper_open_scale, right_gripper_open_scale
- **gripper_mode_state**: int32
- Dimensions: left_gripper_mode, right_gripper_mode
- **gripper_mode_action**: int32
- Dimensions: left_gripper_mode, right_gripper_mode
- **gripper_activity_state**: int32
- Dimensions: left_gripper_activity, right_gripper_activity

### Meta Information

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

```json
{"codebase_version": "v2.1", "robot_type": "agilex_cobot_decoupled_magic", "total_episodes": 98, "total_frames": 23991, "total_tasks": 1, "total_videos": 294, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:97"}, "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.cam_high_rgb": {"dtype": "video", "shape": [480, 640, 3], "names": ["height", "width", "channels"], "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.cam_left_wrist_rgb": {"dtype": "video", "shape": [480, 640, 3], "names": ["height", "width", "channels"], "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.cam_right_wrist_rgb": {"dtype": "video", "shape": [480, 640, 3], "names": ["height", "width", "channels"], "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.state": {"dtype": "float32", "shape": [26], "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_gripper_open", "left_eef_pos_x_m", "left_eef_pos_y_m", "left_eef_pos_z_m", "left_eef_rot_euler_x_rad", "left_eef_rot_euler_y_rad", "left_eef_rot_euler_z_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_gripper_open", "right_eef_pos_x_m", "right_eef_pos_y_m", "right_eef_pos_z_m", "right_eef_rot_euler_x_rad", "right_eef_rot_euler_y_rad", "right_eef_rot_euler_z_rad"]}, "action": {"dtype": "float32", "shape": [26], "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_gripper_open", "left_eef_pos_x_m", "left_eef_pos_y_m", "left_eef_pos_z_m", "left_eef_rot_euler_x_rad", "left_eef_rot_euler_y_rad", "left_eef_rot_euler_z_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_gripper_open", "right_eef_pos_x_m", "right_eef_pos_y_m", "right_eef_pos_z_m", "right_eef_rot_euler_x_rad", "right_eef_rot_euler_y_rad", "right_eef_rot_euler_z_rad"]}, "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}, "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]}, "gripper_open_scale_state": {"names": ["left_gripper_open_scale", "right_gripper_open_scale"], "dtype": "float32", "shape": [2]}, "gripper_open_scale_action": {"names": ["left_gripper_open_scale", "right_gripper_open_scale"], "dtype": "float32", "shape": [2]}, "gripper_mode_state": {"names": ["left_gripper_mode", "right_gripper_mode"], "dtype": "int32", "shape": [2]}, "gripper_mode_action": {"names": ["left_gripper_mode", "right_gripper_mode"], "dtype": "int32", "shape": [2]}, "gripper_activity_state": {"names": ["left_gripper_activity", "right_gripper_activity"], "dtype": "int32", "shape": [2]}}}
```

### Directory Structure

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

```
Cobot_Magic_move_the_ball_and_the_cube_block_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.cam_high_rgb/
│ ├── episode_000000.mp4
│ ├── episode_000001.mp4
│ ├── episode_000002.mp4
│ ├── episode_000003.mp4
│ ├── episode_000004.mp4
│ └── (...)
├── observation.images.cam_left_wrist_rgb/
│ ├── episode_000000.mp4
│ ├── episode_000001.mp4
│ ├── episode_000002.mp4
│ ├── episode_000003.mp4
│ ├── episode_000004.mp4
│ └── (...)
└── observation.images.cam_right_wrist_rgb/
├── 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

# Cobot_Magic_move_the_ball_and_the_cube_block

## 📋 数据集概览
本数据集采用基于LeRobot的扩展格式，且完全兼容LeRobot。

**机器人类型：** `Cobot_Magic`
**代码库版本：** `v2.1`
**末端执行器类型：** `双指夹持器`

## 🏠 场景类型
本数据集涵盖以下场景类型：
- `home`（家用场景）

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

## 📊 数据集统计
| 指标 | 数值 |
|--------|--------|
| 总情节数 | 98 |
| 总帧数 | 23991 |
| 总任务数 | 1 |
| 总视频数 | 294 |
| 总块数 | 1 |
| 块大小 | 1000 |
| 帧率（FPS） | 30 |
| 数据集总大小 | 451.4MB |

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

## 🔗 相关链接
- 🏠 主页：[https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/)
- 📄 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)
- 🌐 项目页面：[https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/)
- 🐛 问题反馈：[https://github.com/FlagOpen/RoboCOIN/issues](https://github.com/FlagOpen/RoboCOIN/issues)
- 📜 许可证：apache-2.0

## 🏷️ 数据集标签
- `RoboCOIN`
- `LeRobot`

## 🎯 任务描述
### 主要任务
将球体拾取并放置在立方体上方的圆形凹槽中。

### 子任务
本数据集包含9个独立子任务：
1. **End** → 结束
2. **Grasp the sphere on the table** → 抓取桌面上的球体
3. **null** → 无
4. **Pick up the ball** → 拾取球体
5. **Pick up the sphere recess** → 拾取球形凹槽
6. **Place the ball into the sphere recess** → 将球体放入球形凹槽
7. **Place the ball on the table** → 将球体放置在桌面上
8. **Place the sphere into the sphere-shaped recess** → 将球体放入球形凹槽
9. **Place the sphere recess in the center of the table** → 将球形凹槽放置在桌面中央

## 🎥 相机视角
本数据集包含3个相机视角。

## 🏷️ 可用标注
本数据集包含丰富的标注信息，可支持多种机器学习任务：

### 子任务标注
- 子任务分割与标注

### 场景标注
- 语义场景分类与描述

### 末端执行器标注
- 运动方向分类
- 速度幅值分类
- 加速度幅值分类

### 夹持器标注
- 夹持器模式（开合/闭合状态）
- 夹持器活动状态
- 夹持器开合度

### 附加特征
- 末端执行器仿真位姿
- gripper相关特征

## 📂 数据划分
本数据集包含以下数据划分：
- **训练集**：情节索引0~97

## 📁 数据集结构
本数据集遵循LeRobot格式，包含以下组件：

### 数据文件
- 视频文件：包含RGB相机观测的压缩AV1视频，帧率30FPS
- 状态数据：包含机器人关节位置、速度及其他状态信息
- 动作数据：包含机器人动作命令与轨迹
- 元数据：包含情节元数据、时间戳及标注信息

### 文件组织
- 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`
- 块大小：1000

### 特征模式
本数据集包含以下特征：
1. 视觉观测：`observation.images.cam_high_rgb`、`observation.images.cam_left_wrist_rgb`、`observation.images.cam_right_wrist_rgb`，均为AV1编码，帧率30FPS
2. 状态数据：`observation.state`（float32，shape [26]），包含左/右机械臂关节角度、夹持器开合度、末端执行器位姿等信息
3. 动作数据：`action`（float32，shape [26]），与状态数据维度一致
4. 时间信息：`timestamp`、`frame_index`、`episode_index`、`index`、`task_index`
5. 标注信息：`subtask_annotation`、`scene_annotation`
6. 运动特征：`eef_sim_pose_state`、`eef_sim_pose_action`、`eef_direction_state`、`eef_direction_action`、`eef_velocity_state`、`eef_velocity_action`、`eef_acc_mag_state`、`eef_acc_mag_action`
7. 夹持器特征：`gripper_open_scale_state`、`gripper_mode_state`、`gripper_activity_state`

## 📞 联系与支持
关于本数据集的疑问、问题或反馈，请联系：
- 邮箱：无

### 技术支持
请在GitHub仓库提交Issue获取技术支持。

## 📄 许可证
本数据集基于apache-2.0许可证发布，请参阅LICENSE文件。

## 📚 引用要求
若您在研究中使用本数据集，请引用以下论文：
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, Guocai Yao, 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},
journal={arXiv preprint arXiv:2511.17441},
url={https://arxiv.org/abs/2511.17441},
year={2025}
}

### 额外引用
若使用LeRobot框架，请引用：
- LeRobot Framework: https://github.com/huggingface/lerobot

## 📌 版本信息
版本历史：
- v1.0.0 (2025-11)：首次发布