R1_Lite_pick_up_and_store_items

# R1_Lite_pick_up_and_store_items

## 📋 Overview

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

**Robot Type:** `R1_Lite`
| **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`
- `pull`
- `push`

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total Episodes** | 108 |
| **Total Frames** | 125588 |
| **Total Tasks** | 1 |
| **Total Videos** | 324 |
| **Total Chunks** | 1 |
| **Chunk Size** | 1000 |
| **FPS** | 30 |
| **Dataset Size** | 5.8GB |

## 👥 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
Open the drawer, then close the drawer.

### Sub-Tasks
This dataset includes 41 distinct subtasks:

1. **abnormal**
2. **Close the drawer**
3. **Close the drawer with the both gripper**
4. **End**
5. **null**
6. **Open the drawer**
7. **Open the drawer with the both gripper**
8. **Pick up the adhesive tape on the table**
9. **Pick up the disposable slippers on the table**
10. **Pick up the mineral water on the table**
11. **Pick up the roll paper on the table**
12. **Pick up the shorts on the table**
13. **Pick up the solid drinks on the table**
14. **Pick up the T-shirt on the table**
15. **Pick up the tissue on the table**
16. **Place the adhesive tape on the table**
17. **Place the T-shirt on the table**
18. **Place the disposable slippers on the table**
19. **Place the mineral water on the table**
20. **Place the roll paper on the table**
21. **Place the shorts on the table**
22. **Place the solid drinks on the table**
23. **Place the tissue on the table**
24. **Put the adhesive tape in the drawer**
25. **Put the disposable slippers in the drawer**
26. **Put the mineral water in the drawer**
27. **Put the roll paper in the drawer**
28. **Put the shorts in the drawer**
29. **Put the slippers in the drawer**
30. **Put the solid drinks in the drawer**
31. **Put the T-shirt in the drawer**
32. **Put the tissue in the drawer**
33. **Take out the adhesive tape from the drawer**
34. **Take out the disposable slippers from the drawer**
35. **Take out the mineral water from the drawer**
36. **Take out the roll paper from the drawer**
37. **Take out the shorts from the drawer**
38. **Take out the solid drinks from the drawer**
39. **Take out the T-shirt from the drawer**
40. **Take out the tissue from the drawer**
41. **Take the paper out of the drawer and put it on 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 0:107

## 📁 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": "galaxea_r1_lite", "total_episodes": 108, "total_frames": 125588, "total_tasks": 1, "total_videos": 324, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:107"}, "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": [720, 1280, 3], "names": ["height", "width", "channels"], "info": {"video.height": 720, "video.width": 1280, "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": [720, 1280, 3], "names": ["height", "width", "channels"], "info": {"video.height": 720, "video.width": 1280, "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": [720, 1280, 3], "names": ["height", "width", "channels"], "info": {"video.height": 720, "video.width": 1280, "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": [14], "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", "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"]}, "action": {"dtype": "float32", "shape": [14], "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", "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"]}, "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):

```
R1_Lite_pick_up_and_store_items_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