FastUMIPro/example_data_fastumi_pro_raw

FastUMI Pro Dataset

Enterprise-grade Robotic Manipulation Dataset for Universal Manipulation Interface

Project Homepage | 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

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	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:

Data	Description
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

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☎️ 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。

负责人 (Lead)	Ding Yan
Email	[email protected]
WeChat	Duke_dingyan

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