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RoboCOIN
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RMC-AIDA-L_desktop_organization

Tasks:
Robotics
Modalities:
Tabular
Time-series
Video
Formats:
parquet
Languages:
English
Chinese
Size:
1M - 10M
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Tags:
RoboCOIN
LeRobot
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RMC-AIDA-L_desktop_organization

📋 Overview

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

Robot Type: realman_rmc_aidal | 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

📊 Dataset Statistics

Metric Value
Total Episodes 1660
Total Frames 2647489
Total Tasks 2
Total Videos 4980
Total Chunks 2
Chunk Size 1000
FPS 30
Dataset Size 38.7GB

👥 Authors

Contributors

This dataset is contributed by:

🔗 Links

🏷️ Dataset Tags

  • RoboCOIN
  • LeRobot

🎯 Task Descriptions

Primary Tasks

put the orange in the fruit plate, put the paper ball in the trash can, stand the bottle upright, put the art knife in the pen holder, put the glue in the pen holder, and put the eraser in the pen holder. put the peach in the fruit plate, put the plastic in the trash can, stand the bottle upright, put the ruler in the pen holder, put the pen in the pen holder, and put the scissor in the pen holder.

Sub-Tasks

This dataset includes 63 distinct subtasks:

  1. Place the blue scissors into the pen holder with the right hand.
  2. Grab the orange with the left hand.
  3. Place the blue scissors into the pen holder with the left hand.
  4. Pass the ruler from the right hand to the left hand.
  5. Place the gray spring-action pen into the pen holder with the left hand.
  6. Pass the blue utility knife from the left hand to the right hand.
  7. Put the eraser into the pen holder with the right hand.
  8. Grab the blue scissors with the right hand.
  9. Grab the blue utility knife with the left hand.
  10. Place the orange into the fruit bowl with the right hand.
  11. Grab the plastic with the right hand.
  12. Discard.
  13. Pass the water bottle from the left hand to the right hand.
  14. Pass the ruler from the left hand to the right hand.
  15. Place the gray spring-action pen into the pen holder with the right hand.
  16. Pass the water bottle from the right hand to the left hand.
  17. Place the eraser into the pen holder with the left hand.
  18. Grab the blue scissors with the left hand.
  19. Grab the ruler with the left hand.
  20. Grab the eraser with the right hand.
  21. Pass the peach from the right hand to the left hand.
  22. Pass the blue scissors from the right hand to the left hand.
  23. Pass the gray spring-action pen from the left hand to the right hand.
  24. Pass the eraser from the left hand to the right hand.
  25. Place the blue utility knife into the pen holder with the right hand.
  26. Place the blue utility knife into the pen holder with the left hand.
  27. Place the ruler into the pen holder with the left hand.
  28. Grab the orange with the right hand.
  29. Grab the plastic with the left hand.
  30. Grab the peach with the right hand.
  31. Place the paper ball into the trash can with the right hand.
  32. Place the orange into the fruit bowl with the left hand.
  33. Grab the gray glue with the right hand.
  34. Pass the plastic from the right hand to the left hand.
  35. Grab the water bottle with the left hand.
  36. Pass the gray glue from the left hand to the right hand.
  37. Pass the paper ball from the left hand to the right hand.
  38. Pass the plastic from the left hand to the right hand.
  39. Grab the blue utility knife with the right hand.
  40. Pass the orange from the right hand to the left hand.
  41. Grab the paper ball with the left hand.
  42. Pass the blue scissors from the left hand to the right hand.
  43. Place the ruler into the pen holder with the right hand.
  44. Place the peach into the fruit bowl with the right hand.
  45. Place the water bottle on the table with the left hand.
  46. Grab the eraser with the left hand.
  47. Pass the gray glue from the right hand to the left hand.
  48. Grab the gray spring-action pen with the left hand.
  49. Pass the blue utility knife from the right hand to the left hand.
  50. Place the peach into the fruit bowl with the left hand.
  51. Grab the gray spring-action pen with the right hand.
  52. Grab the gray glue with the left hand.
  53. Place the water bottle on the table with the right hand.
  54. Grab the ruler with the right hand.
  55. Grab the water bottle with the right hand.
  56. Place the gray glue into the pen holder with the right hand.
  57. Grab the peach with the left hand.
  58. Place the plastic into the trash can with the right hand.
  59. Pass the gray spring-action pen from the right hand to the left hand.
  60. Place the gray glue into the pen holder with the left hand.
  61. Grab the paper ball with the right hand.
  62. end
  63. null

🎥 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 0:1659

📁 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 2 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": "realman_rmc_aidal", "total_episodes": 1660, "total_frames": 2647489, "total_tasks": 2, "total_videos": 4980, "total_chunks": 2, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:1659"}, "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": [28], "names": ["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", "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", "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", "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"]}, "action": {"dtype": "float32", "shape": [28], "names": ["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", "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", "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", "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"]}, "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):

RMC-AIDA-L_desktop_organization_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
│   │   └── (...)
│   └── chunk-001/
│       ├── episode_001000.parquet
│       ├── episode_001001.parquet
│       ├── episode_001002.parquet
│       ├── episode_001003.parquet
│       ├── episode_001004.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
    │       └── (...)
    └── chunk-001/
        ├── observation.images.cam_high_rgb/
        │   ├── episode_001000.mp4
        │   ├── episode_001001.mp4
        │   ├── episode_001002.mp4
        │   ├── episode_001003.mp4
        │   ├── episode_001004.mp4
        │   └── (...)
        ├── observation.images.cam_left_wrist_rgb/
        │   ├── episode_001000.mp4
        │   ├── episode_001001.mp4
        │   ├── episode_001002.mp4
        │   ├── episode_001003.mp4
        │   ├── episode_001004.mp4
        │   └── (...)
        └── observation.images.cam_right_wrist_rgb/
            ├── episode_001000.mp4
            ├── episode_001001.mp4
            ├── episode_001002.mp4
            ├── episode_001003.mp4
            ├── episode_001004.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:

📌 Version Information

Version History

  • v1.0.0 (2025-11): Initial release
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