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observation.state
list
action
list
timestamp
float32
frame_index
int64
episode_index
int64
index
int64
task_index
int64
subtask_indices
list
state_eef_pos_and_rot_euler
list
action_eef_pos_and_rot_euler
list
subtask_annotation
list
scene_annotation
list
eef_direction_state
list
eef_velocity_state
list
eef_acc_mag_state
list
eef_direction_action
list
eef_velocity_action
list
eef_acc_mag_action
list
eef_sim_pose_state
list
eef_sim_pose_action
list
gripper_mode_state
list
gripper_activity_state
list
gripper_mode_action
list
gripper_activity_action
list
gripper_open_scale_state
list
gripper_open_scale_action
list
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End of preview. Expand in Data Studio

RMC-AIDA-L_basket_storage_banana

πŸ“‹ Overview

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

Robot Type: RMC-AIDA-L | Codebase Version: v2.1 End-Effector Type: two_finger_gripper

🏠 Scene Types

This dataset covers the following scene types:

  • home
  • kitchen

πŸ€– Atomic Actions

This dataset includes the following atomic actions:

  • grasp
  • pick
  • place

πŸ“Š Dataset Statistics

Metric Value
Total Episodes 654
Total Frames 306335
Total Tasks 4
Total Videos 1962
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

the left gripper pick up the basket to the appropriate position, the right gripper pick up the banana and place it into the basket. the right gripper pick up the black basket and place it horizontally in the middle of the table, the left gripper pick up the banana and place it into the black basket. the left gripper pick up the black basket and place it horizontally in the middle of the table, the right gripper pick up the banana and place it into the black basket. the right gripper pick up the yellow basket and place it horizontally in the middle of the table, the left gripper pick up the banana and place it into the yellow basket.

Sub-Tasks

This dataset includes 18 distinct subtasks:

  1. abnormal
  2. end
  3. null
  4. Pick up the banana with the left gripper
  5. Pick up the banana with the right gripper
  6. Pick up the basket with the left gripper
  7. Pick up the basket with the right gripper
  8. Pick up the dark basket with the left gripper
  9. Pick up the dark basket with the right gripper
  10. Pick up the light basket with the right gripper
  11. Place the banana into the basket with the right gripper
  12. Place the banana into the dark basket with the left gripper
  13. Place the banana into the dark basket with the right gripper
  14. Place the banana into the light basket with the left gripper
  15. Place the basket in the center of view with the left gripper
  16. Place the dark basket in the center of view with the left gripper
  17. Place the dark basket in the center of view with the right gripper
  18. Place the light basket in the center of view with the right gripper

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 0:653

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_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": 654, "total_frames": 306335, "total_tasks": 4, "total_videos": 1962, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": {"train": "0:653"}, "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_basket_storage_banana_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_high_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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