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RoboCOIN
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R1_Lite_pick_up_and_store_items

Tasks:
Robotics
Modalities:
Tabular
Time-series
Video
Formats:
parquet
Languages:
English
Chinese
Size:
100K - 1M
ArXiv:
Tags:
RoboCOIN
LeRobot
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
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R1_Lite_pick_up_and_store_items

📋 Overview

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

Robot Type: galaxea_r1_lite | 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
  • pull
  • push

📊 Dataset Statistics

Metric Value
Total Episodes 108
Total Frames 125588
Total Tasks 1
Total Videos 324
Total Chunks 1
Chunk Size 1000
FPS 30
Dataset Size 5.8GB

👥 Authors

Contributors

This dataset is contributed by:

🔗 Links

🏷️ Dataset Tags

  • RoboCOIN
  • LeRobot

🎯 Task Descriptions

Primary Tasks

Open the drawer, then close the drawer.

Sub-Tasks

This dataset includes 41 distinct subtasks:

  1. Place the tissue on the table
  2. Place the disposable slippers on the table
  3. Take out the tissue from the drawer
  4. Put the slippers in the drawer
  5. Take out the mineral water from the drawer
  6. Open the drawer with the both gripper
  7. Place the roll paper on the table
  8. Place the shorts on the table
  9. Place the adhesive tape on the table
  10. Take out the disposable slippers from the drawer
  11. Place the solid drinks on the table
  12. Take out the adhesive tape from the drawer
  13. Put the shorts in the drawer
  14. Close the drawer with the both gripper
  15. End
  16. Pick up the tissue on the table
  17. Put the tissue in the drawer
  18. Take out the shorts from the drawer
  19. Pick up the T-shirt on the table
  20. Take out the solid drinks from the drawer
  21. Pick up the mineral water on the table
  22. Put the mineral water in the drawer
  23. Pick up the adhesive tape on the table
  24. Take out the roll paper from the drawer
  25. Close the drawer
  26. Open the drawer
  27. Take out the T-shirt from the drawer
  28. Put the disposable slippers in the drawer
  29. Put the adhesive tape in the drawer
  30. Take the paper out of the drawer and put it on the table
  31. Put the solid drinks in the drawer
  32. Place the mineral water on the table
  33. Put the T-shirt in the drawer
  34. abnormal
  35. Place the T-shirt on the table
  36. Pick up the solid drinks on the table
  37. Pick up the disposable slippers on the table
  38. Pick up the shorts on the table
  39. Pick up the roll paper on the table
  40. Put the roll paper in the drawer
  41. 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 0:107

📁 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": "galaxea_r1_lite", "total_episodes": 108, "total_frames": 125588, "total_tasks": 1, "total_videos": 324, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:107"}, "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": [720, 1280, 3], "names": ["height", "width", "channels"], "info": {"video.height": 720, "video.width": 1280, "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": [720, 1280, 3], "names": ["height", "width", "channels"], "info": {"video.height": 720, "video.width": 1280, "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": [720, 1280, 3], "names": ["height", "width", "channels"], "info": {"video.height": 720, "video.width": 1280, "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": [14], "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", "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"]}, "action": {"dtype": "float32", "shape": [14], "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", "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"]}, "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):

R1_Lite_pick_up_and_store_items_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:

📌 Version Information

Version History

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