Update README.md

README.md

@@ -4,19 +4,37 @@ language:
   - zh
 tags:
   - robotics
-  - embodied-ai
   - manipulation
   - multimodal
-  - vla
-  - data-collection
+  - trajectory-data
+  - vision-sensors
 license: other
 task_categories:
   - robotics
-  - imitation-learning
-multimodal: vision+depth+trajectory+force
-configs:
-  - config_name: default
-    data_files: "**/*"
+multimodal: vision+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: "**/*"
 ---
 
 <h1 align="center" style="font-size: 40px; font-weight: bold;">
@@ -35,11 +53,36 @@ configs:
 </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
+## 📖 Overview
 
-**VLA Model Results**: [TBD]
+The **FastUMI Pro Sample Dataset** contains a small number of demonstration trajectories  
+(only **dozens of episodes**, *not* a large-scale dataset).  
+It showcases the multimodal sensing capabilities of the FastUMI Pro system, including:
+
+- RGB camera streams  
+- Visual SLAM pose trajectories  
+- Vive tracking data  
+- Time-of-Flight (ToF) point clouds  
+- Clamp (gripper gap) measurements  
+- Fused pose trajectories  
+
+This dataset is intended as a **public preview** of the data modality, structure, and quality of FastUMI Pro.  
+For full-scale datasets or customized collection services, please **contact the FastUMI team directly**.
+
+---
+
+## ✨ Key Features
+
+- **High-precision spatial tracking**  
+- **Multi-sensor synchronization** across RGB, SLAM, Vive, ToF, and clamp channels  
+- **Standardized directory and timestamp structure**  
+- **Ready for embodied AI, imitation learning, and robotics research**  
+- **Hardware-agnostic data format** for cross-platform manipulation applications  
+
+---
+
+<!-- **VLA Model Results**: [TBD]
 
 ## 🛠️ Toolchain
 | Tool | Description | Link |
@@ -49,10 +92,16 @@ configs:
 | **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
+<!-- ### 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