Datasets:

FastUMIPro
/

example_data_fastumi_pro_raw

@@ -39,175 +39,33 @@ dataset_info:
       data_files: "**/*"
 ---
-# Fast-UMI: A Scalable and Hardware-Independent Universal Manipulation Interface
-**Welcome to the official repository of FastUMI Pro!**
-[![Viewed](https://img.shields.io/badge/Viewed-100%2B-blue)](#)
-[![All](https://img.shields.io/badge/All-Data-green)](#)
-[![Pattern](https://img.shields.io/badge/Pattern-Multi--modal-orange)](#)
-[![New](https://img.shields.io/badge/New-4s-brightgreen)](#)
-[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97-HuggingFace-yellow)](https://huggingface.co/datasets/FastUMI)
-[![GitHub](https://img.shields.io/badge/GitHub-Repository-black)](https://github.com/FastUMI)
-[![FastUMI Data](https://img.shields.io/badge/FastUMI-Data-purple)](#)
-[Project Page](https://fastumi.com/pro/) |
-[Hugging Face Dataset](https://huggingface.co/datasets/FastUMI) |
-[PDF (Early Version)](https://arxiv.org/abs/2409.19499) |
-[PDF (TBA)](#)
-*FastUMI Pro dataset document*
-<br>
-## 📋 Contents
-| Section | Description |
-|---------|-------------|
-| [🎯 Project Description](#-project-description) | Overview and introduction |
-| [📊 Dataset Overview](#-dataset-overview) | Key features and capabilities |
-| [🚀 Quick Start](#-quick-start) | Get started quickly |
-| [📁 Dataset Structure](#-dataset-structure) | Data organization and format |
-| [⚙️ Data Specifications](#️-data-specifications) | Technical details and attributes |
-| [🔄 Data Conversion](#-data-conversion) | Format conversion tools |
-| [📰 News](#-news) | Latest updates |
-| [📄 License](#-license) | Usage terms |
-| [📞 Contact](#-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
-```bash
-# 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](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.
-multi_sessions_{time}_{serial number}
-└──session_001
-    └── 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
-└──session_002
-└──session_003
-└──session_004
-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
-📄 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]
-FastUMI Pro - Advancing Robot Manipulation Through Scalable Data Systems

       data_files: "**/*"
 ---
+📚 FastUMI Pro 数据集文档FastUMI Pro 是 FastUMI 的升级版，面向企业用户的流程化、全链路数据采集与转换系统。💡 项目简介FastUMI（Fast Universal Manipulation Interface）是一个面向通用机器人操作任务的数据集与接口框架，旨在支持硬件无关、可扩展的高效数据采集与模型训练。该项目提供了物理原型系统、完整的数据采集代码、标准化数据格式和使用工具，便于研究者进行真实环境下的操作学习研究。🌐 项目主页： https://fastumi.com/pro/🔗 FastUMI 主页： https://fastumi.com💾 数据样例： https://huggingface.co/datasets/FastUMIPro/example_data_fastumi_pro_raw🤝 合作使用优先说明FastUMI Pro 在 FastUMI 的基础上，增设提供：更高的精度轨迹数据。更多样化的本体支持，真正面向“一脑多形”。FastUMI-150K 曾作为完整版本开源，包括约 150,000 条真实世界操作轨迹，已提供给合作研究团队用于训练大规模 VLA（视觉-语言-动作）模型。初步实验表明，基于该数据集训练的模型在通用操控任务中展现出显著的多任务泛化能力。🚀 VLA 模型效果速递模型效果速递：[TBD]🔗 开源工具链工具名称描述GitHub 链接Demo Replay 单臂仓库FastUMI 数据单臂回放代码GitHub - Loki-Lu/FastUMI_replay_singleARMDemo Replay 双臂仓库FastUMI 数据双臂回放代码GitHub - Loki-Lu/FastUMI_replay_dualARM硬件 SDKFastUMI 硬件相关的软件开发工具包GitHub - FastUMIRobotics/FastUMI_Hardware_SDK监控工具用于实时监控 FastUMI 设备的工具GitHub - FastUMIRobotics/FastUMI_Monitor_Tool数据采集工具使用 FastUMI 设备采集数据的工具GitHub - FastUMIRobotics/FastUMI_Data_Collection相关论文与应用论文： [2508.10538] MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm教程： PI0（FastUMI数据轻量级适配，版本V0）全流程教程📦 数据使用指��样例数据下载样例数据链接：https://huggingface.co/datasets/FastUMIPro/example_data_fastumi_pro_raw/tree/main方式一：原始命令（国内可能较慢）Bashhuggingface-cli download FastUMIPro/example_data_fastumi_pro_raw --repo-type dataset --local-dir ~/fastumi_data/
+方式二：镜像加速方案（推荐）Bash# 设置镜像端点
+export HF_ENDPOINT=https://hf-mirror.com
+# 使用镜像下载
 huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/
+📁 数据结构说明每个 session 文件代表一个独立的操作 “episode”（片段），包含观测数据与动作序列。FastUMI PRO 数据格式为 raw，包含各类原始数据，方便查询、校验各传感器的原始输出，易于转换为其他任意格式。目录结构示例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
+└── session_002
+└── ...
+属性说明数据项文件路径形状 / 类型含义与顺序RGB_ImagesFrames/frame_...jpg(帧数, 1080, 1920, 3), uint8, 60.0 FPS摄像头视频数据。SLAM_Posesslam_raw.txt(时刻数, 7), 浮点数据集UMI 末端位姿数据。Vive_Posesvive_data_tum.txt(时刻数, 7), 浮点数据集Vive 基站的姿态数据。ToF_PointCloudsPointClouds/pointcloud_...pcdpcd 格式ToF 点云原始数据。Clamp_Dataclamp_data_tum.txt(时刻数, 1), 浮点数据集夹爪间距，单位：mm。Merged_Trajectorymerged_trajectory.txt(时刻数, 7), 浮点数据集融合后的轨迹数据（基准根据速度选择 Vive 或 UMI）。位姿数据的通用顺序（SLAM, Vive, Merged）：$$[Pos\ X, \ Pos\ Y, \ Pos\ Z, \ Q\_X, \ Q\_Y, \ Q\_Z, \ Q\_W]$$📝 数据转换方法[TBD]✉️ 联系方式如有任何问题或建议，欢迎联系开发团队：负责人： 丁琰邮箱： [email protected]： Duke_dingyan