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observation.state
listlengths
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action
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2.3
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End of preview. Expand in Data Studio

Cobot_Magic_fold_clothes

πŸ“‹ Overview

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

Robot Type: Cobot_Magic | Codebase Version: v2.1 End-Effector Type: two_finger_gripper

🏠 Scene Types

This dataset covers the following scene types:

  • home

πŸ€– Atomic Actions

This dataset includes the following atomic actions:

  • grasp
  • place
  • pick

πŸ“Š Dataset Statistics

Metric Value
Total Episodes 584
Total Frames 774487
Total Tasks 6
Total Videos 1752
Total Chunks 1
Chunk Size 1000
FPS 30

πŸ‘₯ Authors

Contributors

This dataset is contributed by:

πŸ”— Links

🏷️ Dataset Tags

  • RoboCOIN
  • LeRobot

🎯 Task Descriptions

Primary Tasks

fold a grey long-sleeved shirt over a blue coffee tablecloth. fold a grey long-sleeved shirt over the brown coffee tablecloth. fold a grey long-sleeved shirt over a green coffee tablecloth. fold a grey long-sleeved shirt over a pink coffee tablecloth. fold a grey long-sleeved shirt over a white checkered tablecloth. fold a grey long-sleeved shirt on a white desktop.

Sub-Tasks

This dataset includes 20 distinct subtasks:

  1. Abnormal
  2. Drag the left edge of the fabric to the left with your left arm and fold it to the right edge
  3. End
  4. Hold the left edge of the fabric with your left arm
  5. null
  6. Spread the clothes out flat on the table with both grippers
  7. Spread the long sleeves with your arms and place them on the tabletop
  8. use both arms simultaneously to clamp the lower edge of the clothing fabric
  9. use both arms to drag the lower edge of the fabric forward and fold it over the upper edge
  10. use the left arm to clamp the cuff closest to it
  11. use the left arm to clamp the cuff farthest from it
  12. use the left arm to drag and drop the cuff onto the cuff closest to the left arm
  13. use the left arm to drag and place the cuff at the centered position on the left edge of the fabric
  14. use the right arm to clamp the right edge of the fabric
  15. use the right arm to drag and drop the cuff onto the cuffclosest to the right arm
  16. use the right arm to drag and place the cuff at thecentered position on the left edge of the fabric
  17. use the right arm to drag the right edge of the fabric to the left and folds it over the left edge
  18. use the right arm to drag the right edge of the fabric to the right and folds it over the left edge
  19. Use your right arm to clamp the cuff closest to him
  20. Use your right arm to clamp the cuff farthest to him

πŸŽ₯ 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

πŸ“‚ Data Splits

The dataset is organized into the following splits:

  • Training: Episodes 0:583

πŸ“ 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_front_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": 584, "total_frames": 774487, "total_tasks": 6, "total_videos": 1752, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:583"}, "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_front_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):

Cobot_Magic_fold_clothes_qced_hardlink/
β”œβ”€β”€ annotations/
β”‚   β”œβ”€β”€ eef_acc_mag_annotation.jsonl
β”‚   β”œβ”€β”€ eef_direction_annotation.jsonl
β”‚   β”œβ”€β”€ eef_velocity_annotation.jsonl
β”‚   β”œβ”€β”€ gripper_activity_annotation.jsonl
β”‚   β”œβ”€β”€ gripper_mode_annotation.jsonl
β”‚   └── (...)
β”œβ”€β”€ data/
β”‚   └── chunk-000/
β”‚       β”œβ”€β”€ episode_000000.parquet
β”‚       β”œβ”€β”€ episode_000001.parquet
β”‚       β”œβ”€β”€ episode_000002.parquet
β”‚       β”œβ”€β”€ episode_000003.parquet
β”‚       β”œβ”€β”€ episode_000004.parquet
β”‚       └── (...)
β”œβ”€β”€ meta/
β”‚   β”œβ”€β”€ episodes.jsonl
β”‚   β”œβ”€β”€ episodes_stats.jsonl
β”‚   β”œβ”€β”€ info.json
β”‚   └── tasks.jsonl
└── videos/
    └── chunk-000/
        β”œβ”€β”€ observation.images.cam_front_rgb/
        β”‚   β”œβ”€β”€ episode_000000.mp4
        β”‚   β”œβ”€β”€ episode_000001.mp4
        β”‚   β”œβ”€β”€ episode_000002.mp4
        β”‚   β”œβ”€β”€ episode_000003.mp4
        β”‚   β”œβ”€β”€ episode_000004.mp4
        β”‚   └── (...)
        β”œβ”€β”€ observation.images.cam_left_wrist_rgb/
        β”‚   β”œβ”€β”€ episode_000000.mp4
        β”‚   β”œβ”€β”€ episode_000001.mp4
        β”‚   β”œβ”€β”€ episode_000002.mp4
        β”‚   β”œβ”€β”€ episode_000003.mp4
        β”‚   β”œβ”€β”€ episode_000004.mp4
        β”‚   └── (...)
        └── observation.images.cam_right_wrist_rgb/
            β”œβ”€β”€ episode_000000.mp4
            β”œβ”€β”€ episode_000001.mp4
            β”œβ”€β”€ episode_000002.mp4
            β”œβ”€β”€ episode_000003.mp4
            β”œβ”€β”€ episode_000004.mp4
            └── (...)

πŸ“ž 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.

πŸ“„ License

This dataset is released under the apache-2.0 license.

Please refer to the LICENSE file for full license terms and conditions.

πŸ“š 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:

πŸ“Œ Version Information

Version History

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