Update README.md

README.md

@@ -1,45 +1,3 @@
----
-language:
-  - en
-  - zh
-tags:
-  - robotics
-  - manipulation
-  - vla
-  - trajectory-data
-  - multimodal
-  - vision-language-action
-license: other
-task_categories:
-  - robotics
-  - reinforcement-learning
-  - computer-vision
-multimodal: vision+language+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: "**/*"
----
-
 # FastUMI Pro Dataset
 
 ## Project Description
@@ -67,4 +25,146 @@ huggingface-cli download FastUMIPro/example_data_fastumi_pro_raw --repo-type dat
 
 # Mirror acceleration solution
 export HF_ENDPOINT=https://hf-mirror.com
-huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/
+huggingface-cli download --repo-type dataset --resume-download FastUMIPro/example_data_fastumi_pro_raw --local-dir ~/fastumi_data/
+
+## Data 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.
+DATA/
+└── 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
+
+### 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
+
+Model Performance
+Preliminary experiments show that models trained on this dataset demonstrate significant multi-task generalization capabilities in universal manipulation tasks:
+
+VLA Models: Including PI-O models with language understanding and action planning capabilities, exhibiting excellent generalization and execution stability in multi-task language-conditioned control
+
+VA Models: Classical visual control architectures like ACT, DP also show significant improvements, particularly in complex operation sequences, viewpoint perturbations, and fine motion tracking with enhanced robustness
+
+Related Links
+Project Homepage: https://fastumi.com/pro/
+
+FastUMI Project: https://fastumi.com
+
+Hugging Face Dataset: https://huggingface.co/datasets/IPE...
+
+Research Paper: [2409.19499] FastUMI: A Scalable and...
+
+Open Source Toolchain:
+
+Demo Replay: GitHub - Loki-Lu/FastUMI_replay_sin...
+
+Dual-arm Demo: GitHub - Loki-Lu/FastUMI_replay_du...
+
+Hardware SDK: GitHub - FastUMIRobotics/FastUMI_...
+
+Monitoring Tools: GitHub - FastUMIRobotics/FastUMI_...
+
+Data Collection Tools: GitHub - FastUMIRobotics/FastUMI_...
+
+Related Research
+[2508.10538] MLM: Learning Multi-ta...
+
+PIO (FastUMI Lightweight Adaptation, Version V0) Full Tutorial: PIO (FastUMI数据轻量级适配，版本V0)…
+
+Citation
+If you use this dataset in your research, please cite the relevant papers:
+
+bibtex
+@article{fastumi2024,
+  title={FastUMI: A Scalable and Hardware-Agnostic Framework for Robot Manipulation Learning},
+  author={FastUMI Team},
+  journal={arXiv preprint},
+  year={2024}
+}
+Contact
+For any questions or suggestions, please contact the development team:
+
+Lead: [Name]
+
+Email: [Email Address]
+
+WeChat: [WeChat ID]