--- title: G1edu-u3_pick_plastic_bowl_ac canonical_url: "https://www.modelscope.cn/datasets/RoboCOIN/G1edu-u3_pick_plastic_bowl_ac" md_url: "https://www.modelscope.cn/datasets/RoboCOIN/G1edu-u3_pick_plastic_bowl_ac.md" repository: RoboCOIN/G1edu-u3_pick_plastic_bowl_ac last_updated: 2025-11-29 license: apache-2.0 storage_size: "176 MB" downloads: 6103 stars: 0 --- # G1edu-u3_pick_plastic_bowl_ac > G1edu-u3_pick_plastic_bowl_ac - RoboCOIN 在 ModelScope 开源的数据集。G1edu-u3pickplasticbowlac RoboCOIN/G1edu-u3_pick_plastic_bowl_ac 是 ModelScope 魔搭社区上的数据集，存储大小 176 MB，采用 apache-2.0 许可。 - **Repository**: RoboCOIN/G1edu-u3_pick_plastic_bowl_ac - **License**: apache-2.0 - **Storage size**: 176 MB - **Downloads**: 6103 - **Stars**: 0 - **Last updated**: 2025-11-29 Source: https://www.modelscope.cn/datasets/RoboCOIN/G1edu-u3_pick_plastic_bowl_ac --- # G1edu-u3_pick_plastic_bowl_ac ## 📋 Overview This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot. **Robot Type:** `G1edu-u3` | **Codebase Version:** `v2.1` **End-Effector Type:** `three_finger_hand` ## 🏠 Scene Types This dataset covers the following scene types: - `home` ## 🤖 Atomic Actions This dataset includes the following atomic actions: - `grasp` - `pick` ## 📊 Dataset Statistics | Metric | Value | |--------|-------| | **Total Episodes** | 32 | | **Total Frames** | 12573 | | **Total Tasks** | 1 | | **Total Videos** | 32 | | **Total Chunks** | 1 | | **Chunk Size** | 32 | | **FPS** | 30 | | **Dataset Size** | 175.8MB | ## 👥 Authors ### Contributors This dataset is contributed by: - [RoboCOIN](https://flagopen.github.io/RoboCOIN/) - RoboCOIN Team ## 🔗 Links - **🏠 Homepage:** [https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/) - **📄 Paper:** [https://arxiv.org/abs/2511.17441](https://arxiv.org/abs/2511.17441) - **💻 Repository:** [https://github.com/FlagOpen/RoboCOIN](https://github.com/FlagOpen/RoboCOIN) - **🌐 Project Page:** [https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/) - **🐛 Issues:** [https://github.com/FlagOpen/RoboCOIN/issues](https://github.com/FlagOpen/RoboCOIN/issues) - **📜 License:** apache-2.0 ## 🏷️ Dataset Tags - `RoboCOIN` - `LeRobot` ## 🎯 Task Descriptions ### Primary Tasks Pick up the crumpled paper Pick up the empty bottle Pick up the leftover food Pick up the metal bowl Pick up the metal bowl Pick up the plastic bowl Pick up the plastic bowl Place the metal bowl Place the metal bowl Place the plastic bowl Place the plastic bowl ### Sub-Tasks This dataset includes 4 distinct subtasks: 1. **End** 2. **Grasp the plastic bowl and lift it to the center of the view with left gripper** 3. **Grasp the plastic bowl and lift it to the center of the view with right gripper** 4. **null** ## 🎥 Camera Views This dataset includes 1 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 0:31 ## 📁 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 32 ### Features Schema The dataset includes the following features: #### Visual Observations - **observation.images.ego_view**: video - FPS: 30 - Codec: h264 #### State and Action- **observation.state**: float32- **action**: float32 #### Temporal Information - **timestamp**: float64 - **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 ### Meta Information The complete dataset metadata is available in [meta/info.json](meta/info.json): ```json {"codebase_version": "v2.1", "robot_type": null, "total_episodes": 32, "total_frames": 12573, "total_tasks": 1, "total_videos": 32, "total_chunks": 1, "chunks_size": 32, "fps": 30, "splits": {"train": "0:31"}, "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.ego_view": {"dtype": "video", "shape": [480, 640, 3], "names": ["height", "width", "channel"], "info": {"video.height": 480, "video.width": 640, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false}}, "observation.state": {"dtype": "float32", "shape": [30], "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_arm_joint_7_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_arm_joint_7_rad", "left_hand_joint_1_rad", "left_hand_joint_2_rad", "left_hand_joint_3_rad", "left_hand_joint_4_rad", "left_hand_joint_5_rad", "left_hand_joint_6_rad", "left_hand_joint_7_rad", "right_hand_joint_1_rad", "right_hand_joint_2_rad", "right_hand_joint_3_rad", "right_hand_joint_4_rad", "right_hand_joint_5_rad", "right_hand_joint_6_rad", "right_hand_joint_7_rad"]}, "action": {"dtype": "float32", "shape": [30], "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_arm_joint_7_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_arm_joint_7_rad", "left_hand_joint_1_rad", "left_hand_joint_2_rad", "left_hand_joint_3_rad", "left_hand_joint_4_rad", "left_hand_joint_5_rad", "left_hand_joint_6_rad", "left_hand_joint_7_rad", "right_hand_joint_1_rad", "right_hand_joint_2_rad", "right_hand_joint_3_rad", "right_hand_joint_4_rad", "right_hand_joint_5_rad", "right_hand_joint_6_rad", "right_hand_joint_7_rad"]}, "timestamp": {"dtype": "float64", "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]}}, "discarded_episode_indices": []} ``` ### Directory Structure The dataset is organized as follows (showing leaf directories with first 5 files only): ``` G1edu-u3_pick_plastic_bowl_ac_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.ego_view/ ├── 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: ```bibtex @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 ## Version History - v1.0.0 (2025-11): Initial release