Galbot_g1_steamer_storage_baozi_h
收藏魔搭社区2025-12-05 更新2025-12-06 收录
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https://modelscope.cn/datasets/RoboCOIN/Galbot_g1_steamer_storage_baozi_h
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资源简介：
# Galbot_g1_steamer_storage_baozi_h
## 📋 Overview
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
**Robot Type:** `Galbot_g1`
| **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`
- `place`
- `pick`
## 📊 Dataset Statistics
| Metric | Value |
|--------|-------|
| **Total Episodes** | 979 |
| **Total Frames** | 957982 |
| **Total Tasks** | 1 |
| **Total Videos** | 2937 |
| **Total Chunks** | 1 |
| **Chunk Size** | 1000 |
| **FPS** | 30 |
| **Dataset Size** | 17.8GB |
## 👥 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
take steamed baozi put them in steamer.
### Sub-Tasks
This dataset includes 6 distinct subtasks:
1. **End**
2. **Grasp the baozi in the plate with right gripper**
3. **Grasp the pot lid with left gripper**
4. **null**
5. **Place the baozi on the steamer with right gripper**
6. **Place the pot lid on the steamer with left gripper**
## 🎥 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 0:978
## 📁 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": "yinhe", "total_episodes": 979, "total_frames": 957982, "total_tasks": 1, "total_videos": 2937, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:978"}, "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": [368, 640, 3], "names": ["height", "width", "channels"], "info": {"video.height": 368, "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": [368, 640, 3], "names": ["height", "width", "channels"], "info": {"video.height": 368, "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": [49], "names": ["body_joint_1_rad", "body_joint_2_rad", "body_joint_3_rad", "head_joint_1_rad", "head_joint_2_rad", "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", "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_arm_joint_7_rad", "right_gripper_open", "left_arm_joint_1_vel_rad_s", "left_arm_joint_2_vel_rad_s", "left_arm_joint_3_vel_rad_s", "left_arm_joint_4_vel_rad_s", "left_arm_joint_5_vel_rad_s", "left_arm_joint_6_vel_rad_s", "left_arm_joint_7_vel_rad_s", "left_arm_joint_1_eff_nm", "left_arm_joint_2_eff_nm", "left_arm_joint_3_eff_nm", "left_arm_joint_4_eff_nm", "left_arm_joint_5_eff_nm", "left_arm_joint_6_eff_nm", "left_arm_joint_7_eff_nm", "right_arm_joint_1_vel_rad_s", "right_arm_joint_2_vel_rad_s", "right_arm_joint_3_vel_rad_s", "right_arm_joint_4_vel_rad_s", "right_arm_joint_5_vel_rad_s", "right_arm_joint_6_vel_rad_s", "right_arm_joint_7_vel_rad_s", "right_arm_joint_1_eff_nm", "right_arm_joint_2_eff_nm", "right_arm_joint_3_eff_nm", "right_arm_joint_4_eff_nm", "right_arm_joint_5_eff_nm", "right_arm_joint_6_eff_nm", "right_arm_joint_7_eff_nm"]}, "action": {"dtype": "float32", "shape": [16], "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", "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_arm_joint_7_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):
```
Galbot_g1_steamer_storage_baozi_h_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
# Galbot_g1_steamer_storage_baozi_h
## 📋 数据集概览
本数据集基于LeRobot扩展格式构建，完全兼容LeRobot框架。
**机器人类型(Robot Type):** `Galbot_g1`
**代码库版本(Codebase Version):** `v2.1`
**末端执行器类型(End-Effector Type):** 双指夹爪(two_finger_gripper)
## 🏠 场景类型
本数据集涵盖家庭场景(`home`)。
## 🤖 原子动作集
本数据集包含抓取(grasp)、放置(place)、拾取(pick)三类原子动作。
## 📊 数据集统计信息
| 指标(Metric) | 数值(Value) |
|--------|-------|
| 总回合数(Total Episodes) | 979 |
| 总帧数(Total Frames) | 957982 |
| 总任务数(Total Tasks) | 1 |
| 总视频数(Total Videos) | 2937 |
| 总数据块数(Total Chunks) | 1 |
| 数据块大小(Chunk Size) | 1000 |
| 帧率(FPS) | 30 |
| 数据集总大小(Dataset Size) | 17.8GB |
## 👥 作者信息
### 贡献者
本数据集由[RoboCOIN](https://flagopen.github.io/RoboCOIN/)团队贡献。
## 🔗 相关链接
- 🏠 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)
- 💻 代码仓库(Repository): [https://github.com/FlagOpen/RoboCOIN](https://github.com/FlagOpen/RoboCOIN)
- 🌐 项目页面(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)
- 📜 开源协议(License): apache-2.0
## 🏷️ 数据集标签
- `RoboCOIN`
- `LeRobot`
## 🎯 任务描述
### 主任务
将蒸制包子放入蒸屉中。
### 子任务
本数据集包含6个明确子任务：
1. 结束(End)
2. 使用右侧夹爪抓取餐盘内的包子(Grasp the baozi in the plate with right gripper)
3. 使用左侧夹爪抓取锅盖(Grasp the pot lid with left gripper)
4. 无(null)
5. 使用右侧夹爪将包子放置于蒸屉上(Place the baozi on the steamer with right gripper)
6. 使用左侧夹爪将锅盖放置于蒸屉上(Place the pot lid on the steamer with left gripper)
## 🎥 相机视角
本数据集包含3个相机视角。
## 🏷️ 可用标注集
本数据集包含丰富的标注信息，支撑多样化机器学习研究：
### 子任务标注： 细粒度子任务分割与标注
### 场景标注： 语义化场景分类与描述
### 末端执行器标注： 运动方向、速度幅值、加速度幅值分类
### 夹爪标注： 夹爪开合模式与活动状态分类
### 附加特征：
- 仿真空间末端执行器6D位姿信息（含状态与动作数据）
- 夹爪开合度连续测量值（含状态与动作数据）
## 📂 数据划分
训练集(Training): 回合0:978
## 📁 数据集结构
本数据集遵循LeRobot格式规范，包含以下组件：
### 数据文件
- 视频文件： RGB相机观测的压缩视频
- 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： 回合元数据、时间戳与标注信息
### 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`
- 共1个数据块，单块大小为1000
### 特征模式
#### 视觉观测
- `observation.images.cam_high_rgb`: 高清RGB相机视频，帧率30，编码av1，分辨率480×640
- `observation.images.cam_left_wrist_rgb`: 左侧腕部RGB相机视频，帧率30，编码av1，分辨率368×640
- `observation.images.cam_right_wrist_rgb`: 右侧腕部RGB相机视频，帧率30，编码av1，分辨率368×640
#### 状态与动作数据
- `observation.state`: float32类型
- `action`: float32类型
#### 时间信息
- timestamp: float32
- frame_index: int64
- episode_index: int64
- index: int64
- task_index: int64
#### 标注信息
- `subtask_annotation`: 子任务标注
- `scene_annotation`: 场景标注
#### 运动特征
- `eef_sim_pose_state`/`eef_sim_pose_action`: 仿真空间末端执行器6D位姿
- `eef_direction_state`/`eef_direction_action`: 运动方向分类
- `eef_velocity_state`/`eef_velocity_action`: 运动速度幅值分类
- `eef_acc_mag_state`/`eef_acc_mag_action`: 运动加速度幅值分类
#### 夹爪特征
- `gripper_open_scale_state`/`gripper_open_scale_action`: 夹爪开合度连续测量值
- `gripper_mode_state`/`gripper_mode_action`: 夹爪开合模式标注
- `gripper_activity_state`: 夹爪活动状态分类
## 📄 元信息
完整数据集元数据位于`meta/info.json`，具体结构如下：
json
{"codebase_version":"v2.1","robot_type":"yinhe","total_episodes":979,"total_frames":957982,"total_tasks":1,"total_videos":2937,"total_chunks":1,"chunks_size":1000,"fps":30,"splits":{"train":"0:978"},"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]},"observation.images.cam_left_wrist_rgb":{"dtype":"video","shape":[368,640,3]},"observation.images.cam_right_wrist_rgb":{"dtype":"video","shape":[368,640,3]},"observation.state":{"dtype":"float32","shape":[49]},"action":{"dtype":"float32","shape":[16]},"timestamp":{"dtype":"float32"},"frame_index":{"dtype":"int64"},"episode_index":{"dtype":"int64"},"index":{"dtype":"int64"},"task_index":{"dtype":"int64"},"subtask_annotation":{"dtype":"int32","shape":[5]},"scene_annotation":{"dtype":"int32","shape":[1]},"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]},"gripper_open_scale_state":{"dtype":"float32","shape":[2]},"gripper_open_scale_action":{"dtype":"float32","shape":[2]},"gripper_mode_state":{"dtype":"int32","shape":[2]},"gripper_mode_action":{"dtype":"int32","shape":[2]},"gripper_activity_state":{"dtype":"int32","shape":[2]}}}
## 📂 目录结构
本数据集目录组织如下（仅展示前5个文件）：
Galbot_g1_steamer_storage_baozi_h_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
│ └── (...)
└── observation.images.cam_right_wrist_rgb/
├── episode_000000.mp4
└── (...)
## 📞 联系与支持
如需咨询数据集相关问题、反馈bug或提出改进建议，请联系：
- 邮箱：无
技术支持请在GitHub代码仓库提交issue。
## 📄 开源协议
本数据集采用apache-2.0开源协议发布，完整协议条款请参考仓库内的LICENSE文件。
## 📚 引用信息
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, 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},
year={2025},
url={https://arxiv.org/abs/2511.17441}
}
### 额外引用
若您使用本数据集，还请引用LeRobot框架：[https://github.com/huggingface/lerobot](https://github.com/huggingface/lerobot)
## 📌 版本信息
- v1.0.0 (2025-11): 初始发布版本
提供机构：
maas
创建时间：
2025-11-29
