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- # FastUMI Pro Dataset
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-
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- ## Project Description
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  FastUMI Pro is the upgraded enterprise version of FastUMI, designed for streamlined, end-to-end data acquisition and transformation systems for corporate users.
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  FastUMI (Fast Universal Manipulation Interface) is a dataset and interface framework for universal robot manipulation tasks, supporting hardware-agnostic, scalable, and efficient data collection and model training. The project provides physical prototype systems, complete data collection code, standardized data formats, and utility tools to facilitate real-world manipulation learning research.
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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
@@ -15,7 +101,7 @@ 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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- ## Quick Start
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  ### Download Example Data
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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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- ## Data 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
@@ -59,64 +146,74 @@ DATA/
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  ```
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  ### Directory Descriptions
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- session_xxx: Individual data collection session
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-
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- RGB_Images: Frame images supporting multiple viewpoints; supports both Images and Videos
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-
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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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-
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- - `Shape: (frames, 1920, 1080, 3)`
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-
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- - `Data type: uint8`
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-
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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:
113
 
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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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+ ---
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+ language:
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+ - en
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+ - zh
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+ tags:
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+ - robotics
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+ - manipulation
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+ - vla
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+ - trajectory-data
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+ - multimodal
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+ - vision-language-action
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+ license: other
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+ task_categories:
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+ - robotics
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+ - reinforcement-learning
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+ - computer-vision
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+ multimodal: vision+language+action
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+ dataset_info:
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+ features:
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+ - name: rgb_images
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+ dtype: image
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+ description: Multi-view RGB images
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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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+ sequence: float32
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+ description: Time-of-Flight point cloud data
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+ - name: clamp_data
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+ sequence: float32
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+ description: Clamp sensor readings
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+ - name: merged_trajectory
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+ sequence: float32
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+ description: Fused trajectory data
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+ configs:
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+ - config_name: default
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+ data_files: "**/*"
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+ ---
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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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+
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+ # Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface
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+
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+ **Welcome to the official repository of FastUMI Pro!**
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+
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+ [![Viewed](https://img.shields.io/badge/Viewed-100%2B-blue)](#)
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+ [![All](https://img.shields.io/badge/All-Data-green)](#)
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+ [![Pattern](https://img.shields.io/badge/Pattern-Multi--modal-orange)](#)
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+ [![New](https://img.shields.io/badge/New-4s-brightgreen)](#)
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+ [![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97-HuggingFace-yellow)](https://huggingface.co/datasets/FastUMI)
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+ [![GitHub](https://img.shields.io/badge/GitHub-Repository-black)](https://github.com/FastUMI)
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+ [![FastUMI Data](https://img.shields.io/badge/FastUMI-Data-purple)](#)
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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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+
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+ <br>
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+
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+ <img src="https://via.placeholder.com/600x300/4CB5AE/FFFFFF?text=FastUMI+Prototype+System" alt="FastUMI Prototype" width="600"/>
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+
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+ *Physical prototypes of the Fast-UMI system*
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+
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+ </div>
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+
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+ <br>
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+
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+ ## 📋 Contents
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+
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+ | Section | Description |
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+ |---------|-------------|
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+ | [🎯 Project Description](#-project-description) | Overview and introduction |
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+ | [📊 Dataset Overview](#-dataset-overview) | Key features and capabilities |
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+ | [🚀 Quick Start](#-quick-start) | Get started quickly |
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+ | [📁 Dataset Structure](#-dataset-structure) | Data organization and format |
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+ | [⚙️ Data Specifications](#️-data-specifications) | Technical details and attributes |
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+ | [🔄 Data Conversion](#-data-conversion) | Format conversion tools |
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+ | [📰 News](#-news) | Latest updates |
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+ | [📄 License](#-license) | Usage terms |
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+ | [📞 Contact](#-contact) | Get in touch |
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+
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+ ---
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+
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+ ## 🎯 Project Description
90
 
91
  FastUMI Pro is the upgraded enterprise version of FastUMI, designed for streamlined, end-to-end data acquisition and transformation systems for corporate users.
92
 
93
  FastUMI (Fast Universal Manipulation Interface) is a dataset and interface framework for universal robot manipulation tasks, supporting hardware-agnostic, scalable, and efficient data collection and model training. The project provides physical prototype systems, complete data collection code, standardized data formats, and utility tools to facilitate real-world manipulation learning research.
94
 
95
+ ## 📊 Dataset Overview
96
 
97
  FastUMI Pro builds upon FastUMI with enhanced features:
98
  - Higher precision trajectory data
 
101
 
102
  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.
103
 
104
+ ## 🚀 Quick Start
105
 
106
  ### Download Example Data
107
 
 
114
  huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/
115
  ```
116
 
117
+ ## 📁 Dataset Structure
118
+
119
  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.
120
 
121
  ```plaintext
 
146
  ```
147
 
148
  ### Directory Descriptions
 
 
 
 
 
149
 
150
+ - **`session_xxx`**: Individual data collection session
151
+ - **`RGB_Images`**: Frame images supporting multiple viewpoints; supports both Images and Videos
152
+ - **`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
156
 
157
+ ## ⚙️ Data Specifications
158
 
159
+ ### Attributes
160
 
161
+ - **`sim`**:
162
+ - `False`: Real environment data
163
+ - `True`: Simulation data
164
 
165
+ ### Observations
 
166
 
167
+ - **`observations/images/`**: Camera image data
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+ - Default camera name: `front`
169
+ - Shape: `(frames, 1920, 1080, 3)`
170
+ - Data type: `uint8`
171
+ - Compression: `gzip` (level 4)
172
 
173
+ - **`observations/qpos`**:
174
+ - Type: Floating point dataset
175
+ - Shape: `(timesteps, 7)`
176
+ - Meaning: Robot end-effector position + quaternion orientation
177
+ - Order: `[Pos X, Pos Y, Pos Z, Q_X, Q_Y, Q_Z, Q_W]`
178
 
179
+ ### Actions
 
180
 
181
+ - Type: Floating point dataset
182
+ - Shape: `(timesteps, 7)`
183
+ - Meaning: Actions (same structure as qpos, typically mirroring qpos)
 
 
 
 
184
 
185
+ ## 🔄 Data Conversion
186
 
187
+ Supports one-click export to specific formats via web toolchain, or conversion between formats using tools like:
188
 
189
+ - **Any4lerobot**: [GitHub - Tavish9/any4lerobot](https://github.com/Tavish9/any4lerobot)
190
 
191
+ Conversion paths supported:
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+ - hdf5 → lerobot v3.0
193
+ - hdf5 → lerobot(Pi0) v2.0
194
+ - hdf5 → rlds
195
 
196
+ ## 📰 News
197
 
198
+ - **[2024-12]** We released Data Collection Code and Dataset.
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+ - **[2024-11]** FastUMI Pro enterprise version announced.
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+ - **[2024-10]** Initial FastUMI-150K dataset released to research partners.
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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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+ ---
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+ <div align="center">
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+ **FastUMI Pro** - *Advancing Robot Manipulation Through Scalable Data Systems*
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+ </div>