README.md

---
license: cc-by-nc-4.0
task_categories:
  - robotics
tags:
  - lerobot
  - hand-pose
  - rgb-d
  - humanoid
  - manipulation
  - 6dof
  - mediapipe
  - egocentric
  - motion-semantics
size_categories:
  - 10K<n<100K
language:
  - en
pretty_name: Dynamic Intelligence - Egocentric Human Motion Annotation Dataset
---

# Dynamic Intelligence - Egocentric Human Motion Annotation Dataset

RGB-D hand manipulation dataset captured with iPhone 13 TrueDepth sensor for humanoid robot training. Includes 6-DoF hand pose trajectories, synchronized video, and semantic motion annotations.

---

## 📊 Dataset Overview

| Metric | Value |
|--------|-------|
| Episodes | 97 |
| Total Frames | ~28,000 |
| FPS | 30 |
| Tasks | 10 manipulation tasks |
| Total Duration | ~15.5 minutes |
| Avg Episode Length | ~9.6 seconds |

### Task Distribution

| Task ID | Description | Episodes |
|---------|-------------|----------|
| Task 1 | Fold the white t-shirt on the bed | 8 |
| Task 2 | Fold the jeans on the bed | 10 |
| Task 3 | Fold two underwear and stack them | 10 |
| Task 4 | Put the pillow on the right place | 10 |
| Task 5 | Pick up plate and glass, put on stove | 10 |
| Task 6 | Go out the door and close it | 9 |
| Task 7 | Pick up sandals, put next to scale | 10 |
| Task 8 | Put cloth in basket, close drawer | 10 |
| Task 9 | Screw the cap on your bottle | 10 |
| Task 10 | Pick up two objects, put on bed | 10 |

---

## 📁 Repository Structure

```
humanoid-robots-training-dataset/
│
├── data/
│   └── chunk-000/                    # Parquet files (97 episodes)
│       ├── episode_000000.parquet
│       ├── episode_000001.parquet
│       └── ...
│
├── videos/
│   └── chunk-000/rgb/                # MP4 videos (synchronized)
│       ├── episode_000000.mp4
│       └── ...
│
├── meta/                             # Metadata & Annotations
│   ├── info.json                     # Dataset configuration (LeRobot format)
│   ├── stats.json                    # Feature min/max/mean/std statistics
│   ├── events.json                   # Disturbance & recovery annotations
---

## 🎯 Data Schema

### Parquet Columns (per frame)

| Column | Type | Description |
|--------|------|-------------|
| `episode_index` | int64 | Episode number (0-96) |
| `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

Each joint has 6 values: `[x_cm, y_cm, z_cm, yaw_deg, pitch_deg, roll_deg]`

**Coordinate System:**
- Origin: Camera (iPhone TrueDepth)
- X: Right (positive)
- Y: Down (positive)  
- Z: Forward (positive, into scene)

---

## 🏷️ 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

**File:** `meta/events.json`

Disturbances and recovery actions for select episodes.

### Disturbance Types
| Type | Description |
|------|-------------|
| `OCCLUSION` | Hand temporarily blocked from camera |
| `TARGET_MOVED` | Object shifted unexpectedly |
| `SLIP` | Object slipped during grasp |
| `COLLISION` | Unintended contact |
| `DEPTH_DROPOUT` | Depth sensor lost valid readings |

### Recovery Actions
| Action | Description |
|--------|-------------|
| `REGRASP` | Release and re-acquire object |
| `REACH_ADJUST` | Modify approach trajectory |
| `ABORT` | Stop current action |
| `REPLAN` | Compute new action sequence |

---

## 📈 Depth Quality Metrics

| Metric | Description | Dataset Average |
|--------|-------------|-----------------|
| `valid_depth_pct` | % frames with valid depth at hand | 95.5% ✅ |

---

## 🚀 Usage

### With LeRobot

```python
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
```

### Loading Motion Annotations

```python
import json
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",
    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})")
```

---

## 📖 Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{dynamic_intelligence_2024,
  author = {Dynamic Intelligence},
  title = {Egocentric Human Motion Annotation Dataset},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset}
}
```

---

## 📧 Contact

**Email:** shayan@dynamicintelligence.company  
**Organization:** [Dynamic Intelligence](https://dynamicintelligence.company)

---

## 🖼️ 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.

---

## 👁️ Visualizer Tips

When using the [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
- **Full data access**: All joint data is available in the parquet files under `Files and versions`