Cobot_Magic_pour_water_a

📋 Overview

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

Robot Type: agilex_cobot_decoupled_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
place
pick

📊 Dataset Statistics

Metric	Value
Total Episodes	896
Total Frames	1302424
Total Tasks	8
Total Videos	2688
Total Chunks	1
Chunk Size	1000
FPS	30
Dataset Size	13.8GB

👥 Authors

Contributors

This dataset is contributed by:

RoboCOIN - RoboCOIN Team

🔗 Links

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

🏷️ Dataset Tags

RoboCOIN
LeRobot

🎯 Task Descriptions

Primary Tasks

Pick up a paper cup and a bottle of Nongfu Spring mineral water, pour the water into the paper cup, and then place both back on the table. Pick up a paper cup and a Yibao mineral water, pour the water into the paper cup, and then place both back on the table. Pick up a paper cup and a Yibao mineral water, pour the water into the paper cup, and then place both back on the table. Pick up a paper cup and a Nongfu Spring mineral water bottle, pour the water into the paper cup, and then place both back on the table. Pick up a paper cup and a Nongfu Spring water bottle, pour the water into one of them, and then put both down. Pick up a paper cup and a Yibao mineral water, pour water into one of them, and then put both down. Pick up the blue paper cup and the Wahaha mineral water bottle, pour an appropriate amount of water into the paper cup, and then put both down. Pick up the blue paper cup and the Bai Suishan mineral water bottle, pour an appropriate amount of water into the paper cup, and then put both down.

Sub-Tasks

This dataset includes 42 distinct subtasks:

Grab the paper cup on the right.
Lift the paper cup on the right.
Pick up the bottle
Grab the bottle with right gripper
Grab the water bottle on the right.
Pour water from bottle to cup with the right gripper
Abnormal
Place the paper cup with the left gripper
Lift the paper cup on the left.
Place the bottle
Put the cup down with left gripper
Grasp the cup with left gripper
Grasp the bottle with the left gripper
Pour water from bottle to cup with the left gripper
Take the water with left gripper
End
Receive water on the left.
Pick up the cup
Discard.
Collect water on the right.
Pour the water with right gripper
Pour water from bottle to cup
Grab the water bottle on the left.
Put the bottle down with right gripper
Place the water bottle on the right.
Pour water on the left.
Grab the paper cup on the left.
Place the bottle with the left gripper
Place the paper cup with the right gripper
Place the bottle with the right gripper
Place the paper cup on the right.
Receive water on the right.
Lift the cup with left gripper
Grasp the paper cup with the right gripper
Grasp the bottle with the right gripper
Grasp the paper cup with the left gripper
Pour water on the right.
Collect water on the left.
Place the cup
Place the paper cup on the left.
Place the water bottle on the left.
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🎥 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 0:895

📁 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:

{"codebase_version": "v2.1", "robot_type": "agilex_cobot_decoupled_magic", "total_episodes": 896, "total_frames": 1302424, "total_tasks": 8, "total_videos": 2688, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:895"}, "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_pour_water_a_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:

@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_pour_water_a 已完整同步