Agilex_Cobot_Magic_storage_object_red_tablecloth

Dataset Description

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

Task Preview

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Overview

• Total Episodes: 200

• Total Frames: 99000

• FPS: 30

• Dataset Size: 2.88 GB

• Robot Name: Agilex_Cobot_Magic

• End-Effector Type: two_finger_gripper

• Teleoperation Type: Due to some reasons, this dataset temporarily cannot provide the teleoperation type information.

• Sensors: cam_head_rgb, cam_left_wrist_rgb, cam_right_wrist_rgb

• Camera Information: cam_head_rgb; cam_left_wrist_rgb; cam_right_wrist_rgb

• Scene: household->living_room

• Objects: red_table_cloths(unknown), table(unknown), apple(unknown), yellow_lemon(unknown), pomegranate(unknown), bread_dough(unknown), waffle(unknown), green_lemon(unknown), eggplant(unknown), chewing_gum(unknown), chocolate(unknown), mango(unknown), chewing_gum(unknown), mint_candy(unknown), mangosteen(unknown), orange(unknown), bread(unknown), banana(unknown), cake(unknown), beef_cheeseburger(unknown), bowl(unknown), pan(unknown), small_teapot(unknown), small_teacup(unknown), paper_ball(unknown), brown_square_towel(unknown), black_cylindrical_pen_holder(unknown), pink_long_towel(unknown), whiteboard_eraser(unknown), mentholatum_facial_cleanser(unknown), duck(unknown), compass(unknown), bowl(unknown), blue_long_towel(unknown), pearMint candy(unknown), mint_candy(unknown), triangular_bread(unknown), long_bread(unknown), chinese_cabbage(unknown), peach(unknown), can(unknown), bathing_in_flowers(unknown), wok(unknown), red_bull_canned_drink(unknown), eyeglass_case(unknown), coke (Slim Can)(unknown), wahaha_AD_calcium(unknown), brave_the_world_beer(unknown), brave_the_world_beer(unknown), shampoo(unknown), cleanser(unknown), sausage(unknown), french_fries(unknown), purple_trash_bag(unknown), red_date(unknown)

• Task Description: pick up an item with a gripper and place it in a random container on the desktop.

Primary Task Instruction

pick up an item with a gripper and place it in a random container on the desktop.

Robot Configuration

• Robot Name: Agilex_Cobot_Magic
• Codebase Version: v2.1
• End-Effector Type: two_finger_gripper
• Teleoperation Type: Due to some reasons, this dataset temporarily cannot provide the teleoperation type information.

Scene and Objects

Scene Type

household->living_room

Objects

• red_table_cloths(unknown)
• table(unknown)
• apple(unknown)
• yellow_lemon(unknown)
• pomegranate(unknown)
• bread_dough(unknown)
• waffle(unknown)
• green_lemon(unknown)
• eggplant(unknown)
• chewing_gum(unknown)
• chocolate(unknown)
• mango(unknown)
• chewing_gum(unknown)
• mint_candy(unknown)
• mangosteen(unknown)
• orange(unknown)
• bread(unknown)
• banana(unknown)
• cake(unknown)
• beef_cheeseburger(unknown)
• bowl(unknown)
• pan(unknown)
• small_teapot(unknown)
• small_teacup(unknown)
• paper_ball(unknown)
• brown_square_towel(unknown)
• black_cylindrical_pen_holder(unknown)
• pink_long_towel(unknown)
• whiteboard_eraser(unknown)
• mentholatum_facial_cleanser(unknown)
• duck(unknown)
• compass(unknown)
• bowl(unknown)
• blue_long_towel(unknown)
• pearMint candy(unknown)
• mint_candy(unknown)
• triangular_bread(unknown)
• long_bread(unknown)
• chinese_cabbage(unknown)
• peach(unknown)
• can(unknown)
• bathing_in_flowers(unknown)
• wok(unknown)
• red_bull_canned_drink(unknown)
• eyeglass_case(unknown)
• coke (Slim Can)(unknown)
• wahaha_AD_calcium(unknown)
• brave_the_world_beer(unknown)
• brave_the_world_beer(unknown)
• shampoo(unknown)
• cleanser(unknown)
• sausage(unknown)
• french_fries(unknown)
• purple_trash_bag(unknown)
• red_date(unknown)

Task Descriptions

• Standardized Task Description: pick up an item with a gripper and place it in a random container on the desktop.

• Operation Type: Due to some reasons, this dataset temporarily cannot provide the operation type information.

• Environment Type: Due to some reasons, this dataset temporarily cannot provide the environment type information.

Sub-Tasks

This dataset includes 22 distinct subtasks:

1. Place the XX into the blue bowl with the left gripper (Index: 0)
2. Place the XX into the purple pot with the right gripper (Index: 1)
3. Place the XX into the pink pot with the right gripper (Index: 2)
4. Grasp the XX with the right gripper (Index: 3)
5. Place the XX into the purple pot with the left gripper (Index: 4)
6. End (Index: 5)
7. Place the XX into the pink pot with the left gripper (Index: 6)
8. Place the XX into the cyan plate with the right gripper (Index: 7)
9. Place the XX into the cyan plate with the left gripper (Index: 8)
10. Place the XX into the red pot with the left gripper (Index: 9)
11. Place the XX into the pen container with the right gripper (Index: 10)
12. Grasp the XX with the left gripper (Index: 11)
13. Place the XX into the blue bowl with the right gripper (Index: 12)
14. Place the XX into the pink bowl with the left gripper (Index: 13)
15. Place the XX into the red pot with the right gripper (Index: 14)
16. Place the XX into the pink bowl with the right gripper (Index: 15)
17. Place the XX into the white plate with the left gripper (Index: 16)
18. Place the XX into the white plate with the right gripper (Index: 17)
19. Place the XX into the blue plate with the left gripper (Index: 18)
20. Place the XX into the pen container with the left gripper (Index: 19)
21. Place the XX into the blue plate with the right gripper (Index: 20)
22. null (Index: 21)

Atomic Actions

• grasp
• lift
• lower

Hardware and Sensors

Sensors

• cam_head_rgb

• cam_left_wrist_rgb

• cam_right_wrist_rgb

Camera Information

• cam_head_rgb: dtype=video, shape=480x640x3, resolution=640x480, codec=av1, pix_fmt=yuv420p

• cam_left_wrist_rgb: dtype=video, shape=480x640x3, resolution=640x480, codec=av1, pix_fmt=yuv420p

• cam_right_wrist_rgb: dtype=video, shape=480x640x3, resolution=640x480, codec=av1, pix_fmt=yuv420p

Coordinate System

• Definition: right-hand-frame

Dimensions & Units

• Joint Rotation: radian
• End-Effector Rotation: radian
• End-Effector Translation: meter

Dataset Statistics

Metric	Value
Total Episodes	200
Total Frames	99000
Total Tasks	22
Total Videos	600
Total Chunks	1
Chunk Size	1000
FPS	30
State Dimensions	26
Action Dimensions	26
Camera Views	3
Dataset Size	2.88 GB

Data Splits

The dataset is organized into the following splits:

• Training: Episodes 0:199

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-{id}/episode_{id}.parquet
• Video Path Pattern: videos/chunk-{id}/observation.images.cam_left_wrist_rgb/episode_{id}.mp{id}
• Chunking: Data is organized into 1 chunk(s) of size 1000

Data Structure (Tree)

Agilex_Cobot_Magic_storage_object_red_tablecloth_qced_hardlink/
|-- annotations
|   |-- eef_acc_mag_annotation.jsonl
|   |-- eef_direction_annotation.jsonl
|   |-- eef_velocity_annotation.jsonl
|   |-- gripper_activity_annotation.jsonl
|   |-- gripper_mode_annotation.jsonl
|   |-- scene_annotations.jsonl
|   `-- subtask_annotations.jsonl
|-- backup
|   |-- data
|   |   `-- chunk-000
|   `-- meta
|       |-- episodes.jsonl
|       |-- episodes_stats.jsonl
|       |-- info.json
|       `-- tasks.jsonl
|-- data
|   `-- chunk-000
|       |-- episode_000000.parquet
|       |-- episode_000001.parquet
|       |-- episode_000002.parquet
|       |-- episode_000003.parquet
|       |-- episode_000004.parquet
|       |-- episode_000005.parquet
|       |-- episode_000006.parquet
|       |-- episode_000007.parquet
|       |-- episode_000008.parquet
|       |-- episode_000009.parquet
|       |-- episode_000010.parquet
|       `-- episode_000011.parquet
|       `-- ... (188 more entries)
|-- meta
|   |-- episodes.jsonl
|   |-- episodes_stats.jsonl
|   |-- info.json
|   `-- tasks.jsonl
|-- videos
|   `-- chunk-000
|       |-- observation.images.cam_head_rgb
|       |-- observation.images.cam_left_wrist_rgb
|       `-- observation.images.cam_right_wrist_rgb
|-- info.yaml
`-- README.md

Camera Views

This dataset includes 3 camera views: cam_head_rgb, cam_left_wrist_rgb, cam_right_wrist_rgb.

Features (Full YAML)

observation.images.cam_head_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_rot_x
  - left_eef_rot_y
  - left_eef_rot_z
  - right_eef_pos_x
  - right_eef_pos_y
  - right_eef_pos_z
  - right_eef_rot_x
  - right_eef_rot_y
  - right_eef_rot_z
  dtype: float32
  shape:
  - 12
eef_sim_pose_action:
  names:
  - left_eef_pos_x
  - left_eef_pos_y
  - left_eef_pos_z
  - left_eef_rot_x
  - left_eef_rot_y
  - left_eef_rot_z
  - right_eef_pos_x
  - right_eef_pos_y
  - right_eef_pos_z
  - right_eef_rot_x
  - right_eef_rot_y
  - right_eef_rot_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_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
gripper_activity_action:
  names:
  - left_gripper_activity
  - right_gripper_activity
  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

Available Annotations

This dataset includes rich annotations to support diverse learning approaches:

• eef_acc_mag_annotation.jsonl
• eef_direction_annotation.jsonl
• eef_velocity_annotation.jsonl
• gripper_activity_annotation.jsonl
• gripper_mode_annotation.jsonl
• scene_annotations.jsonl
• subtask_annotations.jsonl

Dataset Tags

• RoboCOIN
• LeRobot

Authors

Contributors

This dataset is contributed by:-RoboCOIN Team at Beijing Academy of Artificial Intelligence (BAAI)

Annotators

No annotator information available.

Links

• Homepage: https://flagopen.github.io/RoboCOIN/
• Paper: https://arxiv.org/abs/2511.17441
• Repository: https://github.com/FlagOpen/RoboCOIN

Contact and Support

For questions, issues, or feedback regarding this dataset, please contact us.

Support

For technical support, please open an issue on our GitHub repository.

License

apache-2.0

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

Initial Release