Agilex_Cobot_Magic_move_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: 198

• Total Frames: 100817

• FPS: 30

• Dataset Size: 3.23 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: commercial & convenience->supermarket

• Objects: table(unknown), red_table_cloths(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)

• Task Description: the gripper move the object.

Primary Task Instruction

the gripper move the object.

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

commercial & convenience->supermarket

Objects

• table(unknown)
• red_table_cloths(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)

Task Descriptions

• Standardized Task Description: the gripper move the object.

• 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 122 distinct subtasks:

1. Grasp the pink towel with the right gripper (Index: 0)
2. Place the XX on the table with the left gripper (Index: 1)
3. Place the mint candy on the table with the left gripper (Index: 2)
4. Place the snickers on the table with the right gripper (Index: 3)
5. Grasp the pen container with the right gripper (Index: 4)
6. Grasp the grey towel with the left gripper (Index: 5)
7. Place the eyeglass case on the table with the left gripper (Index: 6)
8. Grasp the white duck with the left gripper (Index: 7)
9. Place the eggplant on the table with the right gripper (Index: 8)
10. Place the blue bowl on the table with the right gripper (Index: 9)
11. Grasp the banana with the left gripper (Index: 10)
12. Place the compasses on the table with the right gripper (Index: 11)
13. Place the sandwich on the table with the left gripper (Index: 12)
14. Place the pink cake on the table with the right gripper (Index: 13)
15. Place the banana on the table with the right gripper (Index: 14)
16. Grasp the compasses with the right gripper (Index: 15)
17. Grasp the orange with the right gripper (Index: 16)
18. Grasp the blue cup with the left gripper (Index: 17)
19. Place the peach on the table with the right gripper (Index: 18)
20. Place the green lemon on the table with the right gripper (Index: 19)
21. Grasp the mint candy with the left gripper (Index: 20)
22. Grasp the sandwich with the right gripper (Index: 21)
23. Grasp the eyeglass case with the left gripper (Index: 22)
24. Place the compasses on the table with the left gripper (Index: 23)
25. Place the eyeglass case on the table with the right gripper (Index: 24)
26. Grasp the square chewing gum with the left gripper (Index: 25)
27. Place the brown towel on the table with the left gripper (Index: 26)
28. Place the blue bowl on the table with the left gripper (Index: 27)
29. Grasp the sandwich biscuit with the right gripper (Index: 28)
30. Place the white blackboard erasure on the table with the left gripper (Index: 29)
31. Grasp the white blackboard erasure with the left gripper (Index: 30)
32. Grasp the snickers with the right gripper (Index: 31)
33. Grasp the eyeglass case with the right gripper (Index: 32)
34. Place the eggplant on the table with the left gripper (Index: 33)
35. Place the mango on the table with the right gripper (Index: 34)
36. Place the hard facial cleanser on the table with the right gripper (Index: 35)
37. Place the mint candy on the table with the right gripper (Index: 36)
38. Grasp the brown towel with the left gripper (Index: 37)
39. Grasp the hard facial cleanser with the left gripper (Index: 38)
40. Grasp the chocolate with the right gripper (Index: 39)
41. Grasp the mangosteen with the left gripper (Index: 40)
42. Grasp the peach with the right gripper (Index: 41)
43. Grasp the brown towel with the right gripper (Index: 42)
44. Grasp the mango with the left gripper (Index: 43)
45. Place the snickers on the table with the left gripper (Index: 44)
46. Grasp the snickers with the left gripper (Index: 45)
47. Grasp the lemon with the left gripper (Index: 46)
48. Place the teapot on the table with the left gripper (Index: 47)
49. Grasp the hard facial cleanser with the right gripper (Index: 48)
50. Grasp the sandwich with the left gripper (Index: 49)
51. Place the white duck on the table with the left gripper (Index: 50)
52. Place the white duck on the table with the right gripper (Index: 51)
53. Grasp the teapot with the right gripper (Index: 52)
54. Place the pink towel on the table with the left gripper (Index: 53)
55. Grasp the hollow ring bread with the right gripper (Index: 54)
56. **Grasp the hollow ring bread with the right gripper ** (Index: 55)
57. Grasp the blue bowl with the left gripper (Index: 56)
58. Place the waffle on the table with the right gripper (Index: 57)
59. Place the pen container on the table with the right gripper (Index: 58)
60. Place the mangosteen on the table with the right gripper (Index: 59)
61. Grasp the eggplant with the right gripper (Index: 60)
62. Grasp the pink cake with the left gripper (Index: 61)
63. Grasp the gray towel with the right gripper (Index: 62)
64. Place the pink towel on the table with the right gripper (Index: 63)
65. Grasp the green lemon with the right gripper (Index: 64)
66. Place the green lemon on the table with the left gripper (Index: 65)
67. Grasp the eggplant with the left gripper (Index: 66)
68. Place the hard facial cleanser on the table with the left gripper (Index: 67)
69. Place the lemon on the table with the left gripper (Index: 68)
70. End (Index: 69)
71. Grasp the white duck with the right gripper (Index: 70)
72. Grasp the white blackboard erasure with the right gripper (Index: 71)
73. Grasp the blue blackboard erasure with the left gripper (Index: 72)
74. Place the sandwich biscuit on the table with the right gripper (Index: 73)
75. Place the sandwich on the table with the right gripper (Index: 74)
76. Place the fruit candy on the table with the right gripper (Index: 75)
77. Place the gray towel on the table with the right gripper (Index: 76)
78. Place the blue cup on the table with the right gripper (Index: 77)
79. Place the pen container on the table with the left gripper (Index: 78)
80. Grasp the compasses with the left gripper (Index: 79)
81. Grasp the waffle with the right gripper (Index: 80)
82. Grasp the blue bowl with the right gripper (Index: 81)
83. Place the pink cake on the table with the left gripper (Index: 82)
84. Abnormal (Index: 83)
85. Place the square chewing gum on the table with the right gripper (Index: 84)
86. Place the blue blackboard erasure on the table with the left gripper (Index: 85)
87. Grasp the pink towel with the left gripper (Index: 86)
88. Grasp the mangosteen with the right gripper (Index: 87)
89. Place the grey towel on the table with the left gripper (Index: 88)
90. Grasp the orange with the left gripper (Index: 89)
91. Place the hollow ring bread on the table with the right gripper (Index: 90)
92. Place the blue cup on the table with the left gripper (Index: 91)
93. Place the orange on the table with the right gripper (Index: 92)
94. Place the teapot on the table with the right gripper (Index: 93)
95. Place the mangosteen on the table with the left gripper (Index: 94)
96. Grasp the fruit candy with the right gripper (Index: 95)
97. Grasp the square chewing gum with the right gripper (Index: 96)
98. Place the white blackboard erasure on the table with the right gripper (Index: 97)
99. Grasp the pink cake with the right gripper (Index: 98)
100. Place the mango on the table with the left gripper (Index: 99)
101. Place the chocolate on the table with the left gripper (Index: 100)
102. **Place the hollow ring bread on the table with the right gripper ** (Index: 101)
103. Grasp the blue blackboard erasure with the right gripper (Index: 102)
104. Place the banana on the table with the left gripper (Index: 103)
105. **Place the hard facial cleanser on the table with the right gripper ** (Index: 104)
106. Place the blue blackboard erasure on the table with the right gripper (Index: 105)
107. Grasp the chocolate with the left gripper (Index: 106)
108. Grasp the mango with the right gripper (Index: 107)
109. Place the orange on the table with the left gripper (Index: 108)
110. Grasp the XX with the left gripper (Index: 109)
111. Place the chocolate on the table with the right gripper (Index: 110)
112. Grasp the blue cup with the right gripper (Index: 111)
113. Grasp the mint candy with the right gripper (Index: 112)
114. Place the brown towel on the table with the right gripper (Index: 113)
115. Grasp the pen container with the left gripper (Index: 114)
116. Grasp the green lemon with the left gripper (Index: 115)
117. Grasp the tea cup with the left gripper (Index: 116)
118. Place the tea cup on the table with the left gripper (Index: 117)
119. Place the square chewing gum on the table with the left gripper (Index: 118)
120. Grasp the teapot with the left gripper (Index: 119)
121. Grasp the banana with the right gripper (Index: 120)
122. null (Index: 121)

Atomic Actions

• rasp
• 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	198
Total Frames	100817
Total Tasks	122
Total Videos	594
Total Chunks	1
Chunk Size	1000
FPS	30
State Dimensions	26
Action Dimensions	26
Camera Views	3
Dataset Size	3.23 GB

Data Splits

The dataset is organized into the following splits:

• Training: Episodes 0:197

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_move_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
|       `-- ... (186 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