--- language: - en - zh tags: - robotics - manipulation - trajectory-data - multimodal - embodied-ai multimodal: vision+language+action license: other task_categories: - robotics 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 – Multimodal Sample Dataset

Small-Scale Demonstration Data from the FastUMI Pro Multimodal Sensing System
(Only Dozens of Trajectories — Full Dataset Available Upon Request)

FastUMI Pro Hardware System

Project Homepage
--- ## 📖 Overview The **FastUMI Pro Sample Dataset** provides a public preview of the multimodal sensing capabilities of the FastUMI Pro data collection system. This release contains **only dozens of sample trajectories** and is intended for: - System testing - Robotics and AI pipeline integration - Preliminary algorithm development - Demonstrating multimodal alignment and synchronization Full-scale datasets are available upon request for research or enterprise collaboration.
⚠️ Note: The current image has been compressed. The new lossless image data is on its way.
--- ## 📊 Data Specifications **Purpose:** The following table is to provide users with an overview of the technical specifications of the dataset. | **Data Type** | **Path** | **Shape** | **Type** | **Description** | |--------------|----------|-----------|----------|-----------------| | RGB Images | RGB_Images/Frames/*.mp4| (H, W, 3) | uint8 | Multi-view RGB images | | ToF PointClouds | ToF_PointClouds/PointClouds/*.pcd | variable | pcd | Time-of-Flight point clouds | | Clamp Data | Clamp_Data/clamp_data_tum.txt | (N, 2) | float | Timestamp + clamp width | | Merged Trajectory | Merged_Trajectory/merged_trajectory.txt | (N, 8) | float | Fused multi-sensor pose | --- ## 🧭 Data Formats All pose data (SLAM, Vive, fused) follow the same structure: ```markdown timestamp x y z qx qy qz qw ``` | Field | Description | Field | Description | | :---: | :---: | :---: | :---: | | timestamp | Unix timestamp | qx | Quaternion X component | | x | Position X (meters) | qy | Quaternion Y component | | y | Position Y (meters) | qz | Quaternion Z component | | z | Position Z (meters) | qw | Quaternion W component | --- ### Coordinate System To ensure correct visualization and control, all pose data adheres to the following right-handed coordinate system (World Frame). * **Origin (0,0,0):** Geometric center of the tracking base stations (World Frame). * 🔴 **X-Axis:** Points Forward (the primary direction of manipulation). * 🟢 **Y-Axis:** Points Right (relative to the workspace). * 🔵 **Z-Axis:** Points Upward (opposite to the direction of gravity).
Coordinate System Visualization
Visual reference for the coordinate system.
Tip: When using simulation environments like ROS or Isaac Gym, ensure your coordinate frame conventions match. You may need to apply a transformation if your framework uses a different "up" axis (e.g., Z-up vs. Y-up).
--- ## 📸 How We Collect Data We collect data using the **FastUMI Pro** hardware suite. This system integrates high-frequency sensors to capture comprehensive multimodal interaction data: * **Visual:** Industrial-grade RGB cameras. * **Spatial:** Time-of-Flight depth sensors for dense 3D reconstruction. * **Haptic/State:** Force-sensitive clamp sensors for precise gripper feedback. --- ## 📥 Download ```bash huggingface-cli download FastUMIPro/example_data_fastumi_pro_raw \ --repo-type dataset \ --local-dir ./fastumi_sample/ ``` Optional: ```bash export HF_ENDPOINT=https://hf-mirror.com ``` --- ## ⚠️ Dataset Scale Notice > [!IMPORTANT] > This dataset contains **only a small number of sample episodes** and is **not intended for large-scale training**. > > For full multimodal datasets or enterprise collaborations, please contact the FastUMI team. --- ## 📞 Contact Lead: **Ding Yan** Email: **dingyan@lumosbot.tech** WeChat: **Duke_dingyan** ---