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README.md
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sequence: float32
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description: SLAM pose trajectories
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sequence: float32
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description: Vive tracking system poses
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- name: point_clouds
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data_files: "**/*"
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---
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<!-- 顶部横幅区域 -->
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<div align="center">
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# Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface
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**Welcome to the official repository of FastUMI Pro!**
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[[Viewed](https://img.shields.io/badge/Viewed-100%2B-blue)](#)
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[!
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[[FastUMI Data](https://img.shields.io/badge/FastUMI-Data-purple)](#)
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[Project Page](https://fastumi.com/pro/) |
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[Hugging Face Dataset](https://huggingface.co/datasets/FastUMI) |
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[PDF (Early Version)](https://arxiv.org/abs/2409.19499) |
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[PDF (TBA)](#)
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*FastUMI Pro dataset document*
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## 📊 Dataset Overview
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FastUMI Pro builds upon FastUMI with enhanced features:
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The original FastUMI open-sourced FastUMI-150K containing approximately 150,000 real-world manipulation trajectories, which was first provided to selected research partners for training large-scale VLA (Vision-Language-Action) models.
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huggingface-cli download FastUMIPro/example_data_fastumi_pro_raw --repo-type dataset --local-dir ~/fastumi_data/
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# Mirror acceleration solution
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export HF_ENDPOINT=https://hf-mirror.com
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huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/
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```
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## 📁 Dataset Structure
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FastUMI PRO uses raw format containing various types of raw sensor data, which can be easily converted to other formats. The raw format facilitates querying and validating original sensor outputs for rapid problem identification.
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```plaintext
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multi_sessions_{time}_{serial number}
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└──session_001
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└── device_label_xv_serial/
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└──session_002
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└──session_003
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└──session_004
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```
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- **`RGB_Images`**: Frame images supporting multiple viewpoints; supports both Images and Videos
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- **`SLAM_Poses`**: UMI pose data
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- **`Vive_Poses`**: Vive tracking system pose data
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- **`ToF_PointClouds`**: Time-of-Flight point cloud raw data (depth)
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- **`Merged_Trajectory`**: Trajectory data
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- `False`: Real environment data
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- `True`: Simulation data
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- Data type: `uint8`
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- Compression: `gzip` (level 4)
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- Type: Floating point dataset
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- Shape: `(timesteps, 7)`
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- Meaning: Robot end-effector position + quaternion orientation
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- Order: `[Pos X, Pos Y, Pos Z, Q_X, Q_Y, Q_Z, Q_W]`
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- Meaning: Actions (same structure as qpos, typically mirroring qpos)
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Supports one-click export to specific formats via web toolchain, or conversion between formats using tools like:
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Conversion paths supported:
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- hdf5 → lerobot v3.0
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- hdf5 → lerobot(Pi0) v2.0
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- hdf5 → rlds
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[License information to be added]
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For any questions or suggestions, please contact the development team:
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- **Lead**: [Name]
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- **Email**: [Email Address]
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- **WeChat**: [WeChat ID]
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- name: slam_poses
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sequence: float32
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description: SLAM pose trajectories
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- name: vive_poses
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sequence: float32
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description: Vive tracking system poses
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- name: point_clouds
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data_files: "**/*"
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---
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# Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface
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**Welcome to the official repository of FastUMI Pro!**
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[](#)
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[](#)
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[](#)
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[](#)
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[](https://huggingface.co/datasets/FastUMI)
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[](https://github.com/FastUMI)
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[](#)
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[Project Page](https://fastumi.com/pro/) |
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[Hugging Face Dataset](https://huggingface.co/datasets/FastUMI) |
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[PDF (Early Version)](https://arxiv.org/abs/2409.19499) |
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[PDF (TBA)](#)
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*FastUMI Pro dataset document*
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## 📊 Dataset Overview
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FastUMI Pro builds upon FastUMI with enhanced features:
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* Higher precision trajectory data
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* Support for more diverse robot embodiments, truly enabling "one-brain-multi-form" applications
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* Comprehensive data leadership in the field
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The original FastUMI open-sourced FastUMI-150K containing approximately 150,000 real-world manipulation trajectories, which was first provided to selected research partners for training large-scale VLA (Vision-Language-Action) models.
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huggingface-cli download FastUMIPro/example_data_fastumi_pro_raw --repo-type dataset --local-dir ~/fastumi_data/
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# Mirror acceleration solution
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export HF_ENDPOINT=[https://hf-mirror.com](https://hf-mirror.com)
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huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/
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📁 Dataset Structure
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FastUMI PRO uses raw format containing various types of raw sensor data, which can be easily converted to other formats. The raw format facilitates querying and validating original sensor outputs for rapid problem identification.
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multi_sessions_{time}_{serial number}
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└──session_001
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└── device_label_xv_serial/
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└──session_002
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└──session_003
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└──session_004
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Directory Descriptions
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session_xxx: Individual data collection session
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RGB_Images: Frame images supporting multiple viewpoints; supports both Images and Videos
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SLAM_Poses: UMI pose data
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Vive_Poses: Vive tracking system pose data
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ToF_PointClouds: Time-of-Flight point cloud raw data (depth)
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Merged_Trajectory: Trajectory data
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⚙️ Data Specifications
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Attributes
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sim:
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False: Real environment data
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True: Simulation data
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Observations
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observations/images/: Camera image data
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Default camera name: front
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Shape: (frames, 1920, 1080, 3)
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Data type: uint8
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Compression: gzip (level 4)
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observations/qpos:
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Type: Floating point dataset
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Shape: (timesteps, 7)
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Meaning: Robot end-effector position + quaternion orientation
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Order: [Pos X, Pos Y, Pos Z, Q_X, Q_Y, Q_Z, Q_W]
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Actions
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Type: Floating point dataset
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Shape: (timesteps, 7)
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Meaning: Actions (same structure as qpos, typically mirroring qpos)
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🔄 Data Conversion
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Supports one-click export to specific formats via web toolchain, or conversion between formats using tools like:
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Any4lerobot: GitHub - Tavish9/any4lerobot
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Conversion paths supported:
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hdf5 → lerobot v3.0
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hdf5 → lerobot(Pi0) v2.0
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hdf5 → rlds
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📰 News
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📄 License
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[License information to be added]
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📞 Contact
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For any questions or suggestions, please contact the development team:
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Lead: [Name]
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Email: [Email Address]
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WeChat: [WeChat ID]
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FastUMI Pro - Advancing Robot Manipulation Through Scalable Data Systems
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