| --- |
| language: |
| - en |
| - zh |
| tags: |
| - robotics |
| - manipulation |
| - vla |
| - trajectory-data |
| - multimodal |
| - vision-language-action |
| license: other |
| task_categories: |
| - robotics |
| - reinforcement-learning |
| multimodal: vision+language+action |
| dataset_info: |
| features: |
| - name: rgb_images |
| dtype: image |
| description: Multi-view RGB images |
| - name: slam_poses |
| sequence: float32 |
| description: SLAM pose trajectories |
| - name: vive_poses |
| sequence: float32 |
| description: Vive tracking system poses |
| - name: point_clouds |
| sequence: float32 |
| description: Time-of-Flight point cloud data |
| - name: clamp_data |
| sequence: float32 |
| description: Clamp sensor readings |
| - name: merged_trajectory |
| sequence: float32 |
| description: Fused trajectory data |
| configs: |
| - config_name: default |
| data_files: "**/*" |
| --- |
| |
| <div align="center"> |
| FastUMI Pro Dataset |
| </div> |
| |
| <div align="center"> |
|
|
|  |
|  |
|  |
|
|
| **Enterprise-grade Robotic Manipulation Dataset for Universal Manipulation Interface** |
|
|
| [Project Homepage](https://fastumi.com/pro/) | [FastUMI Home](https://fastumi.com) | [Example Data](https://huggingface.co/datasets/FastUMIPro/example_data_fastumi_pro_raw) |
|
|
| </div> |
|
|
| ## 📖 Overview |
|
|
| FastUMI (Fast Universal Manipulation Interface) is a dataset and interface framework for general-purpose robotic manipulation tasks, designed to support hardware-agnostic, scalable, and efficient data collection and model training. |
|
|
| The project provides: |
| - Physical prototype systems |
| - Complete data collection codebase |
| - Standardized data formats and utilities |
| - Tools for real-world manipulation learning research |
|
|
| ## 🚀 Features |
|
|
| ### FastUMI Pro Enhancements |
| - ✅ **Higher precision trajectory data** |
| - ✅ **Diverse embodiment support** for true "one-brain-multiple-forms" |
| - ✅ **Enterprise-ready** pipeline and full-link data processing |
|
|
| ### FastUMI-150K |
| - ~150,000 real-world manipulation trajectories |
| - Used by research partners for large-scale VLA (Vision-Language-Action) model training |
| - Demonstrated significant multi-task generalization capabilities |
|
|
| ## 📊 Model Performance |
|
|
|
|
| ## **VLA Model Results**: [TBD] |
|
|
| ## 🛠️ Toolchain |
|
|
| | Tool | Description | Link | |
| |------|-------------|------| |
| | **Single-Arm Demo Replay** | Single-arm data replay code | [GitHub](https://github.com/Loki-Lu/FastUMI_replay_singleARM) | |
| | **Dual-Arm Demo Replay** | Dual-arm data replay code | [GitHub](https://github.com/Loki-Lu/FastUMI_replay_dualARM) | |
| | **Hardware SDK** | FastUMI hardware development kit | [GitHub](https://github.com/FastUMIRobotics/FastUMI_Hardware_SDK) | |
| | **Monitor Tool** | Real-time device monitoring | [GitHub](https://github.com/FastUMIRobotics/FastUMI_Monitor_Tool) | |
| | **Data Collection** | Data collection utilities | [GitHub](https://github.com/FastUMIRobotics/FastUMI_Data_Collection) | |
|
|
| ### Research & Applications |
| - **Paper**: [MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm](https://arxiv.org/abs/2508.10538) |
| - **Tutorial**: PI0 (FastUMI Data Lightweight Adaptation, Version V0) Full Pipeline |
|
|
| ## 📥 Data Download |
|
|
| ### Example Dataset |
| ```bash |
| # Direct download (may be slow in some regions) |
| huggingface-cli download FastUMIPro/example_data_fastumi_pro_raw --repo-type dataset --local-dir ~/fastumi_data/ |
| ``` |
|
|
| Mirror Download (Recommended) |
| ```bash |
| # Set mirror endpoint |
| export HF_ENDPOINT=https://hf-mirror.com |
| ``` |
|
|
| # Download via mirror |
| huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/ |
| 📁 Data Structure |
| Each session represents an independent operation "episode" containing observation data and action sequences. |
| ``` |
| Directory Structure |
| text |
| session_001/ |
| └── device_label_xv_serial/ |
| └── session_timestamp/ |
| ├── RGB_Images/ |
| │ ├── timestamps.csv |
| │ └── Frames/ |
| │ ├── frame_000001.jpg |
| │ └── ... |
| ├── SLAM_Poses/ |
| │ └── slam_raw.txt |
| ├── Vive_Poses/ |
| │ └── vive_data_tum.txt |
| ├── ToF_PointClouds/ |
| │ ├── timestamps.csv |
| │ └── PointClouds/ |
| │ └── pointcloud_000001.pcd |
| ├── Clamp_Data/ |
| │ └── clamp_data_tum.txt |
| └── Merged_Trajectory/ |
| ├── merged_trajectory.txt |
| └── merge_stats.txt |
| ``` |
| |
| ## Data Specifications |
|
|
| | Data Type | Path | Shape| Type | Description | |
| | :--- | :--- | :--- | :--- | :--- | |
| | **RGB Images** | `session_XXX/RGB_Images/Video.MP4` | `(frames, 1080, 1920, 3)`| `uint8`| Camera video data, 60 FPS | |
| | **SLAM Poses** | `session_XXX/SLAM_Poses/slam_raw.txt` | `(timestamps, 7)`| `float` | UMI end-effector poses | |
| | **Vive Poses** | `session_XXX/Vive_Poses/vive_data_tum.txt` | `(timestamps, 7)`| `float` | Vive base station poses | |
| | **ToF PointClouds** | `session_XXX/PointClouds/pointcloud_...pcd` | `pcd format` | pcd | Time-of-Flight point cloud data | |
| | **Clamp Data** | `session_XXX/Clamp_Data/clamp_data_tum.txt` | `(timestamps, 1)`| `float` | Gripper spacing (mm) | |
| | **Merged Trajectory** | `session_XXX/Merged_Trajectory/merged_trajectory.txt` | `(timestamps, 7)`| `float` | Fused trajectory (Vive/UMI based on velocity) | |
|
|
| ### Pose Data Format |
|
|
| All pose data (SLAM, Vive, Merged) follow the same format: |
|
|
| | **Timestamp** | Unix timestamp of the trajectory data | |
| | :--- | :--- | |
| | **Pos X** | X-coordinate of position (meters) | |
| | **Pos Y** | Y-coordinate of position (meters) | |
| | **Pos Z** | Z-coordinate of position (meters) | |
| | **Q_X** | X-component of orientation quaternion | |
| | **Q_Y** | Y-component of orientation quaternion | |
| | **Q_Z** | Z-component of orientation quaternion | |
| | **Q_W** | W-component of orientation quaternion | |
|
|
| ## 🔄 Data Conversion |
| [TBD] |
|
|
| ## 🤝 Collaboration |
| FastUMI Pro dataset is available for research collaboration. The full FastUMI-150K dataset has been provided to partner research teams for large-scale model training. |
|
|
| ## 📞 Contact |
| *** |
| |
| > ### ☎️ 开发团队联系方式 |
| > |
| > 对于任何问题或建议，请随时联系我们的开发团队。 |
| > | 角色 | 详情 | |
| > | :--- | :--- | |
| > | **负责人 (Lead)** | Ding Yan | |
| > | **Email** | [dingyan@lumosbot.tech](mailto:dingyan@lumosbot.tech) | |
| > | **WeChat** | `Duke_dingyan` | |
| |
| *** |
