update: full dataset card with taxonomy and usage guide

README.md

@@ -8,18 +8,142 @@ language: ["en", "zh"]
 pretty_name: "ChingMu Robot Motion Dataset"
 tags:
   - motion-capture
-  - humanoid-robots
+  - humanoid-robotics
   - imitation-learning
+  - optical-mocap
+  - bvh
+  - dexterous-hands
+  - whole-body-control
 license: custom
 size_categories: 1M<n
+configs:
+  - config_name: metadata
+    default: true
+    data_files:
+      - split: train
+        path: "metadata/index.csv"
+  - config_name: samples
+    data_files:
+      - split: train
+        path: "samples/**/*"
 ---
 
-# ChingMu Robot Motion Dataset
+# ChingMu 1000-Hour Embodied Motion Dataset
+### 青瞳1000小时具身智能动作数据集
 
-> High-precision optical motion capture data for robot training.
+> High-precision **optical motion capture** data for humanoid robots, dexterous hands, embodied AI, and virtual production.
 
-## Status
-This repository is being prepared. Dataset card, metadata index, and sample files will be added shortly.
+| | |
+|---|---:|
+| **Motion Capture Duration** | **1000+ hours** @ 120 Hz |
+| **Scenarios** | 15+ real-world scenes |
+| **Task categories** | 500+ standardized tasks |
+| **Interactive objects** | 1000+ tracked props (6D pose) |
+| **Modalities** | Full-body skeleton · Finger DoF · Object 6D pose · Multi-view sync video · Task labels |
+| **Output formats** | BVH · Retargeted joint trajectories · NPZ · CSV |
+
+🔒 **Access note:** Metadata, samples, and documentation are publicly browsable. Full raw captures and retargeted datasets are released under a **gated license** — click *Request access* to agree to the license terms and receive download instructions.
+
+---
+
+## Dataset Summary
+
+ChingMu 1000H is a high-precision, multi-modal motion capture dataset built for training and validating embodied AI and humanoid robot controllers.
+
+Data is captured using **optical mocap systems** (sub-mm precision, 120 fps) and covers:
+
+- **Full-body skeleton motion** (23–67 joints, BVH-compatible hierarchies)
+- **Finger & hand motion** (per-hand 20+ DoF, glove + marker hybrid)
+- **Object 6D pose tracking** (rigid-body markers → position + quaternion @120Hz)
+- **Multi-view video** (4–8 synchronized cameras, co-registered with mocap timeline)
+- **Semantic labels**: task name, scenario tag, skill category, quality flag
+
+Scenarios span industrial assembly, household service, retail interaction, healthcare assistance, logistics handling, agricultural work, and staged performance.
+
+### Intended Uses
+- Imitation learning / motion policy training for humanoids
+- Dexterous manipulation datasets (hand-object interaction)
+- Motion generation & retrieval (text/motion cross-modal)
+- Sim-to-real validation (MuJoCo / Isaac Sim via retargeted trajectories)
+- Virtual production & animation reference
+
+---
+
+## Task & Scenario Taxonomy (How to Filter)
+
+All motion takes are indexed in `metadata/index.csv`. The key **filter columns** you'll use most:
+
+| Column | Values (examples) | Use |
+|---|---|---|
+| `scenario` | `industrial`, `household`, `retail`, `healthcare`, `logistics`, `agri`, `performance` | Filter by scene |
+| `task_category` | `locomotion`, `manipulation`, `dexterous_hand`, `tool_use`, `interaction` | Broad category |
+| `task_label` | `walk_carrying_box`, `screw_with_driver`, `pinch_grasp_bottle`, `open_fridge_door` … | Specific task |
+| `has_finger_data` | `true` / `false` | Needs hand DoF? |
+| `has_object_6d` | `true` / `false` | Needs object tracking? |
+| `quality_flag` | `pass` / `warning` / `fail` | Skip bad takes |
+| `retarget_available` | `g1` / `soma` / `none` | Which robot formats exist |
+
+### Full Taxonomy (abridged)
+
+- **Locomotion** → walk, jog, crouch-walk, stairs, uneven-terrain, carry-while-walk
+- **Manipulation (whole-body)** → shelf-pick-place, counter-clear, laundry-move
+- **Dexterous Hand** → pinch, precision-grasp, rotate-lid, insert-plug, button-press, tool-grip
+- **Tool Use** → screwdriver, wrench, scissors, tweezers, spray-bottle
+- **Object Interaction** → door-open/close, drawer, fridge, cabinet, lid-on/off
+- **Social / Contact** → handoff-object, guided-motion, two-person handoff
+- **Performance** → dance, martial-arts-inspired, stylized-gesture
+
+---
+
+## Dataset Structure
+
+After requesting access and downloading:
+---
+
+## Quick Start — Browse & Download
+
+### Step 1: Load the index (NO need to download big files yet)
+### Step 2: Filter by your tags
+### Step 3: Download ONLY the files you selected
+
+> ⚠️ **Don't** `git clone` the whole repo without sparse checkout — the full dataset is very large.
+
+---
+
+## Data Format Specifications
+
+| Property | Value |
+|---|---|
+| Up axis | Y-up |
+| Frame rate | 120 fps |
+| Rotation order | ZYX (BVH standard per-joint) |
+| Units | cm for position, degrees for channels |
+| Joint count | 47–67 depending on finger inclusion |
+
+---
+
+## Quality & Limitations
+
+### Quality controls applied
+- Marker swap detection & auto-correct (per-take)
+- Rigid-body jitter filter (median + threshold)
+- Foot-skating metric computed (flagged when >2cm/s static-foot drift)
+- Gap-fill ≤ 6 frames via cubic spline; longer gaps tagged `quality_flag=warning`
+
+### Known limitations
+- Performer population: currently skewed toward 20–35yo (planned expansion in v2)
+- Finger data precision depends on glove calibration per session
+- Object 6D pose accuracy: ±2mm translation, ±0.5° orientation
+
+### Ethical & Privacy
+- All performers signed informed-consent & appearance release
+- Faces are **not** included in skeleton/metric data
+- No biometric identifier is retained in the released features
+
+---
+
+## Citation
 
 ## Contact
-Questions → open a [Discussion](https://huggingface.co/datasets/ZIHLING/Chingmu-RobotData/discussions)
+- Issues / Requests: [Discussions](https://huggingface.co/datasets/ZIHLING/Chingmu-RobotData/discussions)
+- Email: dataset@chingmu.ai