Update README: 147 episodes (removed 147-149), updated schema & structure

README.md

@@ -25,16 +25,16 @@ RGB-D hand manipulation dataset captured with iPhone 13 TrueDepth sensor for hum
 
 ---
 
-## 📊 Dataset Overview
+## Dataset Overview
 
 | Metric | Value |
 |--------|-------|
-| Episodes | 97 |
-| Total Frames | ~28,000 |
+| Episodes | 147 |
+| Total Frames | ~72,000 |
 | FPS | 30 |
 | Tasks | 10 manipulation tasks |
-| Total Duration | ~15.5 minutes |
-| Avg Episode Length | ~9.6 seconds |
+| Total Duration | ~40 minutes |
+| Avg Episode Length | ~16.3 seconds |
 
 ### Task Distribution
 
@@ -51,101 +51,82 @@ RGB-D hand manipulation dataset captured with iPhone 13 TrueDepth sensor for hum
 | Task 9 | Screw the cap on your bottle | 10 |
 | Task 10 | Pick up two objects, put on bed | 10 |
 
+> **Note:** Task distribution is approximate and will be updated with per-episode language instructions.
+
 ---
 
-## 📁 Repository Structure
+## Repository Structure
 
 ```
 humanoid-robots-training-dataset/
 │
 ├── data/
-│   └── chunk-000/                    # Parquet files (97 episodes)
-│       ├── episode_000000.parquet
-│       ├── episode_000001.parquet
+│   ├── chunk-000/                    # Parquet files (episodes 0-99)
+│   │   ├── episode_000000.parquet
+│   │   └── ...
+│   └── chunk-001/                    # Parquet files (episodes 100-146)
+│       ├── episode_000100.parquet
 │       └── ...
 │
 ├── videos/
-│   └── chunk-000/rgb/                # MP4 videos (synchronized)
-│       ├── episode_000000.mp4
+│   ├── chunk-000/rgb/                # MP4 videos (episodes 0-99)
+│   │   ├── episode_000000.mp4
+│   │   └── ...
+│   └── chunk-001/rgb/                # MP4 videos (episodes 100-146)
+│       ├── episode_000100.mp4
 │       └── ...
 │
 ├── meta/                             # Metadata & Annotations
 │   ├── info.json                     # Dataset configuration (LeRobot format)
 │   ├── stats.json                    # Feature min/max/mean/std statistics
 │   ├── events.json                   # Disturbance & recovery annotations
+│   └── annotations_motion_v1_frames.json  # Motion semantic annotations
+│
+└── README.md
+```
+
 ---
 
-## 🎯 Data Schema
+## Data Schema
 
 ### Parquet Columns (per frame)
 
 | Column | Type | Description |
 |--------|------|-------------|
-| `episode_index` | int64 | Episode number (0-96) |
+| `episode_index` | int64 | Episode number (0-146) |
 | `frame_index` | int64 | Frame within episode |
 | `timestamp` | float64 | Time in seconds |
 | `language_instruction` | string | Task description |
-| `observation.state` | float[252] | 21 hand joints × 2 hands × 6 DoF |
-| `action` | float[252] | Same as state (for imitation learning) |
-| `observation.images.rgb` | struct | Video path + timestamp |
-
-### 6-DoF Hand Pose Format
+| `observation.camera_pose` | float[6] | Camera 6-DoF (x, y, z, roll, pitch, yaw) |
+| `observation.left_hand` | float[9] | Left hand keypoints (wrist + thumb + index) |
+| `observation.right_hand` | float[9] | Right hand keypoints (wrist + index + middle) |
+| `action.camera_delta` | float[6] | Camera delta 6-DoF |
+| `action.left_hand_delta` | float[9] | Left hand delta keypoints |
+| `action.right_hand_delta` | float[9] | Right hand delta keypoints |
+| `rgb` | video | Synchronized RGB video frame |
 
-Each joint has 6 values: `[x_cm, y_cm, z_cm, yaw_deg, pitch_deg, roll_deg]`
+### 6-DoF Format
 
 **Coordinate System:**
 - Origin: Camera (iPhone TrueDepth)
 - X: Right (positive)
-- Y: Down (positive)  
+- Y: Down (positive)
 - Z: Forward (positive, into scene)
 
 ---
 
-## 🏷️ Motion Semantics Annotations
+## Motion Semantics Annotations
 
 **File:** `meta/annotations_motion_v1_frames.json`
 
 Coarse temporal segmentation with motion intent, phase, and error labels.
 
-### Annotation Schema
-
-```json
-{
-  "episode_id": "Task1_Vid2",
-  "segments": [
-    {
-      "start_frame": 54,
-      "end_frame_exclusive": 140,
-      "motion_type": "grasp",           // What action is being performed
-      "temporal_phase": "start",        // start | contact | manipulate | end
-      "actor": "both_hands",            // left_hand | right_hand | both_hands
-      "target": {
-        "type": "cloth_region",         // cloth_region | object | surface
-        "value": "bottom_edge"          // Specific target identifier
-      },
-      "state": {
-        "stage": "unfolded",            // Task-specific state
-        "flatness": "wrinkled",         // For folding tasks only
-        "symmetry": "asymmetric"        // For folding tasks only
-      },
-      "error": "none"                   // misalignment | slip | drop | none
-    }
-  ]
-}
-```
-
 ### Motion Types
 `grasp` | `pull` | `align` | `fold` | `smooth` | `insert` | `rotate` | `open` | `close` | `press` | `hold` | `release` | `place`
 
-### Why Motion Annotations?
-- **Temporal Structure**: Know when manipulation phases begin/end
-- **Intent Understanding**: What the human intends to do, not just kinematics
-- **Error Detection**: Labeled failure modes (slip, drop, misalignment)
-- **Training Signal**: Richer supervision for imitation learning
-
 ---
 
-## 📋 Events Metadata
+## Events Metadata
 
 **File:** `meta/events.json`
 
@@ -170,15 +151,7 @@ Disturbances and recovery actions for select episodes.
 
 ---
 
-## 📈 Depth Quality Metrics
-
-| Metric | Description | Dataset Average |
-|--------|-------------|-----------------|
-| `valid_depth_pct` | % frames with valid depth at hand | 95.5% ✅ |
-
----
-
-## 🚀 Usage
+## Usage
 
 ### With LeRobot
 
@@ -187,63 +160,39 @@ from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 
 dataset = LeRobotDataset("DynamicIntelligence/humanoid-robots-training-dataset")
 
-# Access episode
 episode = dataset[0]
-state = episode["observation.state"]      # [252] hand pose (both hands)
-rgb = episode["observation.images.rgb"]   # Video frame
-task = episode["language_instruction"]    # Task description
+state = episode["observation.camera_pose"]    # [6] camera 6-DoF
+rgb = episode["observation.images.rgb"]       # Video frame
+task = episode["language_instruction"]        # Task description
 ```
 
-### Loading Motion Annotations
+### Direct Parquet Access
 
 ```python
-import json
+import pandas as pd
 from huggingface_hub import hf_hub_download
 
-# Download annotations
 path = hf_hub_download(
     repo_id="DynamicIntelligence/humanoid-robots-training-dataset",
-    filename="meta/annotations_motion_v1_frames.json",
+    filename="data/chunk-000/episode_000000.parquet",
     repo_type="dataset"
 )
-
-with open(path) as f:
-    annotations = json.load(f)
-
-# Get segments for Task1
-task1_episodes = annotations["tasks"]["Task1"]["episodes"]
-for ep in task1_episodes:
-    print(f"{ep['episode_id']}: {len(ep['segments'])} segments")
-```
-
-### Combining Pose + Annotations
-
-```python
-# Get frame-level motion labels
-def get_motion_label(frame_idx, segments):
-    for seg in segments:
-        if seg["start_frame"] <= frame_idx < seg["end_frame_exclusive"]:
-            return seg["motion_type"], seg["temporal_phase"]
-    return None, None
-
-# Example: label each frame
-for frame_idx in range(episode["frame_index"].max()):
-    motion, phase = get_motion_label(frame_idx, episode_annotations["segments"])
-    if motion:
-        print(f"Frame {frame_idx}: {motion} ({phase})")
+df = pd.read_parquet(path)
+print(df.columns.tolist())
+print(f"Frames: {len(df)}")
 ```
 
 ---
 
-## 📖 Citation
+## Citation
 
 If you use this dataset in your research, please cite:
 
 ```bibtex
-@dataset{dynamic_intelligence_2024,
+@dataset{dynamic_intelligence_2025,
   author = {Dynamic Intelligence},
   title = {Egocentric Human Motion Annotation Dataset},
-  year = {2024},
+  year = {2025},
   publisher = {Hugging Face},
   url = {https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset}
 }
@@ -251,25 +200,24 @@ If you use this dataset in your research, please cite:
 
 ---
 
-## 📧 Contact
+## Contact
 
-**Email:** shayan@dynamicintelligence.company  
+**Email:** shayan@dynamicintelligence.company
 **Organization:** [Dynamic Intelligence](https://dynamicintelligence.company)
 
 ---
 
-## 🖼️ Hand Landmark Reference
+## Hand Landmark Reference
 
 ![Hand Landmarks](https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset/resolve/main/assets/hand_landmarks.png)
 
-Each hand has 21 tracked joints. The `observation.state` contains 6-DoF (x, y, z, yaw, pitch, roll) for each joint.
+Each hand has tracked joints. The `observation.left_hand` and `observation.right_hand` contain 3D keypoints for key finger joints.
 
 ---
 
-## 👁️ Visualizer Tips
+## Visualizer
 
-When using the [DI Hand Pose Sample Dataset Viewer](https://huggingface.co/spaces/DynamicIntelligence/dynamic_intelligence_sample_data):
+Explore the dataset interactively: [DI Hand Pose Sample Dataset Viewer](https://huggingface.co/spaces/DynamicIntelligence/dynamic_intelligence_sample_data)
 
-- **Enable plots**: Click the white checkbox next to joint names (e.g., `left_thumb_cmc_yaw_deg`) to show that data in the graph
-- **Why not all enabled by default?**: To prevent browser lag, only a few plots are active initially
+- **Enable plots**: Click the white checkbox next to joint names to show data in the graph
 - **Full data access**: All joint data is available in the parquet files under `Files and versions`