Datasets:

FastUMIPro
/

example_data_fastumi_pro_raw

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: '**/*'

FastUMI Pro Dataset

Enterprise-grade Robotic Manipulation Dataset for Universal Manipulation Interface

Project Homepage | FastUMI Home | Example Data

📖 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

Core Tools

Tool	Description	Link
Single-Arm Demo Replay	Single-arm data replay code	GitHub
Dual-Arm Demo Replay	Dual-arm data replay code	GitHub
Hardware SDK	FastUMI hardware development kit	GitHub
Monitor Tool	Real-time device monitoring	GitHub
Data Collection	Data collection utilities	GitHub

Research & Applications

Paper: MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm
Tutorial: PI0 (FastUMI Data Lightweight Adaptation, Version V0) Full Pipeline

📥 Data Download

Example Dataset

# 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)

# 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	`Frames/frame_...jpg`	`(frames, 1080, 1920, 3)`, `uint8`, 60 FPS	Camera video data
SLAM Poses	`slam_raw.txt`	`(timestamps, 7)`, `float`	UMI end-effector poses
Vive Poses	`vive_data_tum.txt`	`(timestamps, 7)`, `float`	Vive base station poses
ToF PointClouds	`PointClouds/pointcloud_...pcd`	`pcd format`	Time-of-Flight point cloud data
Clamp Data	`clamp_data_tum.txt`	`(timestamps, 1)`, `float`	Gripper spacing (mm)
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:

[Pos_X, Pos_Y, Pos_Z, Q_X, Q_Y, Q_Z, Q_W]

🔄 Data Conversion [TBD - Data conversion methods will be added here]

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

Lead: Ding Yan

Email: [email protected]

WeChat: Duke_dingyan