Datasets:

FastUMIPro
/

example_data_fastumi_pro_raw

	---
	language:
	- en
	- zh
	tags:
	- robotics
	- embodied-ai
	- manipulation
	- multimodal
	- vla
	- data-collection
	license: other
	task_categories:
	- robotics
	- imitation-learning
	multimodal: vision+depth+trajectory+force
	configs:
	- config_name: default
	data_files: "*/"
	---

	<h1 align="center" style="font-size: 40px; font-weight: bold;">
	FastUMI Pro™ Robotics Dataset
	</h1>

	<h3 align="center">
	Enterprise-Grade Data Engine for Embodied AI
	</h3>

	<p align="center">
	<img src="https://img.shields.io/badge/Product-FastUMI_Pro-brightgreen?style=flat"/>
	<img src="https://img.shields.io/badge/Multimodal-7_Sensors-orange?style=flat"/>
	<img src="https://img.shields.io/badge/Trajectory_Data-150K-blue?style=flat"/>
	<img src="https://img.shields.io/badge/Application-VLA_Training-purple?style=flat"/>
	</p>

	---
	## 📖 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](https://github.com/Loki-Lu/FastUMI_replay_singleARM) \|
	\| Dual-Arm Demo Replay \| Dual-arm data replay code \| [GitHub](https://github.com/Loki-Lu/FastUMI_replay_dualARM) \|
	\| Hardware SDK \| FastUMI hardware development kit \| [GitHub](https://github.com/FastUMIRobotics/FastUMI_Hardware_SDK) \|
	\| Monitor Tool \| Real-time device monitoring \| [GitHub](https://github.com/FastUMIRobotics/FastUMI_Monitor_Tool) \|
	\| Data Collection \| Data collection utilities \| [GitHub](https://github.com/FastUMIRobotics/FastUMI_Data_Collection) \|

	### Research & Applications
	- Paper: [MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm](https://arxiv.org/abs/2508.10538)
	- Tutorial: PI0 (FastUMI Data Lightweight Adaptation, Version V0) Full Pipeline

	## 📥 Data Download

	### Example Dataset
	```bash
	# 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)
	```bash
	# 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
	***

	> ### ☎️ 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 \| [dingyan@lumosbot.tech](mailto:dingyan@lumosbot.tech) \|
	> \| WeChat \| `Duke_dingyan` \|

	***