README.md

metadata

language:
  - en
  - zh
tags:
  - robotics
  - manipulation
  - vla
  - trajectory-data
  - multimodal
  - vision-language-action
license: other
task_categories:
  - robotics
  - reinforcement-learning
  - computer-vision
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: '**/*'

Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface

Welcome to the official repository of FastUMI Pro!

Project Page | Hugging Face Dataset | PDF (Early Version) | PDF (TBA)

Physical prototypes of the Fast-UMI system

📋 Contents

Section	Description
🎯 Project Description	Overview and introduction
📊 Dataset Overview	Key features and capabilities
🚀 Quick Start	Get started quickly
📁 Dataset Structure	Data organization and format
⚙️ Data Specifications	Technical details and attributes
🔄 Data Conversion	Format conversion tools
📰 News	Latest updates
📄 License	Usage terms
📞 Contact	Get in touch

---

🎯 Project Description

FastUMI Pro is the upgraded enterprise version of FastUMI, designed for streamlined, end-to-end data acquisition and transformation systems for corporate users.

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.

📊 Dataset Overview

FastUMI Pro builds upon FastUMI with enhanced features:

• Higher precision trajectory data
• Support for more diverse robot embodiments, truly enabling "one-brain-multi-form" applications
• Comprehensive data leadership in the field

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.

🚀 Quick Start

Download Example Data

# Original command (may be slow in some regions)
huggingface-cli download FastUMIPro/example_data_fastumi_pro_raw --repo-type dataset --local-dir ~/fastumi_data/

# Mirror acceleration solution
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/

📁 Dataset Structure

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.

DATA/
└── device_label_xv_serial/
    └── session_timestamp/
        ├── RGB_Images/
        │   ├── timestamps.csv
        │   └── Frames/
        │       ├── frame_000001.jpg
        │       ├── frame_000002.jpg
        │       └── ...
        ├── SLAM_Poses/
        │   └── slam_raw.txt
        ├── Vive_Poses/
        │   └── vive_data_tum.txt
        ├── ToF_PointClouds/
        │   ├── timestamps.csv
        │   └── PointClouds/
        │       ├── pointcloud_000001.pcd
        │       ├── pointcloud_000002.pcd
        │       └── ...
        ├── Clamp_Data/
        │   └── clamp_data_tum.txt
        └── Merged_Trajectory/
            ├── merged_trajectory.txt
            └── merge_stats.txt

Directory Descriptions

• session_xxx: Individual data collection session
• RGB_Images: Frame images supporting multiple viewpoints; supports both Images and Videos
• SLAM_Poses: UMI pose data
• Vive_Poses: Vive tracking system pose data
• ToF_PointClouds: Time-of-Flight point cloud raw data (depth)
• Merged_Trajectory: Trajectory data

⚙️ Data Specifications

Attributes

• sim:
  • False: Real environment data
  • True: Simulation data

Observations

• observations/images/: Camera image data

  • Default camera name: front
  • Shape: (frames, 1920, 1080, 3)
  • Data type: uint8
  • Compression: gzip (level 4)

• observations/qpos:

  • Type: Floating point dataset
  • Shape: (timesteps, 7)
  • Meaning: Robot end-effector position + quaternion orientation
  • Order: [Pos X, Pos Y, Pos Z, Q_X, Q_Y, Q_Z, Q_W]

Actions

• Type: Floating point dataset
• Shape: (timesteps, 7)
• Meaning: Actions (same structure as qpos, typically mirroring qpos)

🔄 Data Conversion

Supports one-click export to specific formats via web toolchain, or conversion between formats using tools like:

• Any4lerobot: GitHub - Tavish9/any4lerobot

Conversion paths supported:

• hdf5 → lerobot v3.0
• hdf5 → lerobot(Pi0) v2.0
• hdf5 → rlds

📰 News

• [2024-12] We released Data Collection Code and Dataset.
• [2024-11] FastUMI Pro enterprise version announced.
• [2024-10] Initial FastUMI-150K dataset released to research partners.

📄 License

[License information to be added]

📞 Contact

For any questions or suggestions, please contact the development team:

• Lead: [Name]
• Email: [Email Address]
• WeChat: [WeChat ID]

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FastUMI Pro - Advancing Robot Manipulation Through Scalable Data Systems