---
license: cc-by-nc-4.0
task_categories:
  - robotics
tags:
  - lerobot
  - hand-pose
  - humanoid
  - manipulation
  - 6dof
  - mediapipe
  - egocentric
  - imitation-learning
size_categories:
  - 10K<n<100K
language:
  - en
pretty_name: Dynamic Intelligence - Humanoid Robot Training Dataset
---

# Dynamic Intelligence — Humanoid Robot Training Dataset

A first-person (egocentric) video dataset of human hand manipulation, designed for training humanoid robot policies via imitation learning. Each episode captures a person performing an everyday household task — folding clothes, moving dishes, opening doors — filmed from a head-mounted iPhone using its built-in LiDAR and depth sensors.

The dataset pairs each video with frame-level 3D hand tracking and camera pose data, giving learning algorithms both the visual input and the corresponding spatial trajectories they need to reproduce the demonstrated behavior on a robot.

---

## How it works

**Recording setup.** A person wears an iPhone 13 Pro on their head (using a head mount). The phone runs the [Record3D](https://record3d.app/) app, which simultaneously captures:
- RGB video at 30 FPS
- Depth maps via the LiDAR sensor
- 6-DoF camera pose from ARKit (position + orientation of the phone in the room)

**Processing pipeline.** After recording, each episode goes through an offline pipeline:
1. **Hand detection** — [MediaPipe](https://ai.google.dev/edge/mediapipe/solutions/vision/hand_landmarker) detects 2D hand landmarks in every RGB frame
2. **3D reconstruction** — The 2D landmarks are projected into 3D space using the corresponding depth map, producing real-world XYZ positions (in cm) relative to the camera
3. **Action computation** — Frame-to-frame deltas are computed for both the camera and hand positions, representing the "actions" a robot would need to take

**Result.** Each episode contains a synchronized video and a parquet file with per-frame 3D observations and actions, formatted for the [LeRobot](https://github.com/huggingface/lerobot) framework.

---

## Dataset overview

| | |
|---|---|
| **Episodes** | 145 |
| **Total data frames** | ~59,000 |
| **Video FPS** | 30 |
| **Tasks** | 12 household manipulation tasks |
| **Format** | [LeRobot v2.0](https://github.com/huggingface/lerobot) |
| **Sensor** | iPhone 13 Pro (RGB + LiDAR + ARKit) |
| **Perspective** | Egocentric (head-mounted) |

### Tasks

| # | Task instruction | Episodes | Count |
|---|------------------|----------|-------|
| 1 | Fold the t-shirt on the bed. | 0–7 | 8 |
| 2 | Pick up the two items on the floor and put them on the bed. | 8–17 | 10 |
| 3 | Fold the jeans on the bed. | 18–27 | 10 |
| 4 | Fold the underwear on the table. | 28–37 | 10 |
| 5 | Put the pillow in its correct place. | 38–47 | 10 |
| 6 | Place the tableware on the kitchen counter. | 48–57 | 10 |
| 7 | Get out of the room and close the door behind you. | 58–66 | 9 |
| 8 | Put the sandals in the right place. | 67–76 | 10 |
| 9 | Put the cleaning cloth in the laundry basket. | 77–86 | 10 |
| 10 | Screw the cap back on the bottle. | 87–95 | 9 |
| 11 | Tuck the chairs into the table. | 96–126 | 31 |
| 12 | Put the dishes in the sink. | 127–144 | 18 |

---

## What's in the data

Each episode has two files: a **video** (`.mp4`) and a **parquet** table with one row per tracked frame.

### Observations (what the robot sees)

| Column | Shape | Unit | Description |
|--------|-------|------|-------------|
| `observation.camera_pose` | float[6] | cm, degrees | Position (x, y, z) and orientation (roll, pitch, yaw) of the head-mounted camera in the room. Comes from ARKit's visual-inertial odometry. |
| `observation.left_hand` | float[9] | cm | 3D positions of 3 keypoints on the left hand: wrist, thumb tip, and index fingertip (x, y, z each). |
| `observation.right_hand` | float[9] | cm | 3D positions of 3 keypoints on the right hand: wrist, index fingertip, and middle fingertip (x, y, z each). |

### Actions (what the robot should do)

| Column | Shape | Description |
|--------|-------|-------------|
| `action.camera_delta` | float[6] | Frame-to-frame change in camera pose (dx, dy, dz, droll, dpitch, dyaw). Represents head movement. |
| `action.left_hand_delta` | float[9] | Frame-to-frame change in left hand keypoint positions. |
| `action.right_hand_delta` | float[9] | Frame-to-frame change in right hand keypoint positions. |

### Metadata columns

| Column | Type | Description |
|--------|------|-------------|
| `episode_index` | int | Which episode (0–144) |
| `frame_index` | int | Frame number within the episode |
| `timestamp` | float | Time in seconds from episode start |
| `language_instruction` | string | Natural language task description (same for all frames in an episode) |
| `next.done` | bool | Whether this is the last frame of the episode |

### Coordinate system

All 3D positions are relative to the camera:
- **X** → right
- **Y** → down
- **Z** → forward (into the scene)

Hand values of `[0, 0, 0]` mean the hand was not detected in that frame (e.g. out of view or occluded).

---

## File structure

```
├── data/
│   ├── chunk-000/          # Parquet files for episodes 0–99
│   └── chunk-001/          # Parquet files for episodes 100–144
├── videos/
│   ├── chunk-000/rgb/      # MP4 videos for episodes 0–99
│   └── chunk-001/rgb/      # MP4 videos for episodes 100–144
├── meta/
│   ├── info.json           # LeRobot dataset config
│   └── stats.json          # Column statistics (min/max/mean/std)
└── README.md
```

---

## Quick start

### With LeRobot

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("DynamicIntelligence/humanoid-robots-training-dataset")

sample = dataset[0]
print(sample["language_instruction"])        # "Fold the t-shirt on the bed."
print(sample["observation.camera_pose"])     # tensor of shape [6]
print(sample["action.left_hand_delta"])      # tensor of shape [9]
```

### Direct download

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="DynamicIntelligence/humanoid-robots-training-dataset",
    filename="data/chunk-000/episode_000000.parquet",
    repo_type="dataset",
)
df = pd.read_parquet(path)
print(f"{len(df)} frames")
print(df[["timestamp", "observation.camera_pose", "language_instruction"]].head())
```

---

## Visualizer

Browse episodes interactively:
**[DI Hand Pose Sample Dataset Viewer](https://huggingface.co/spaces/DynamicIntelligence/dynamic_intelligence_sample_data)**

The viewer shows the egocentric video alongside time-series plots of camera pose and hand positions, so you can see exactly what the person was doing and how the tracking data aligns with the video.

---

## Citation

```bibtex
@dataset{dynamic_intelligence_2025,
  author = {Dynamic Intelligence},
  title = {Humanoid Robot Training Dataset: Egocentric Hand Manipulation Demonstrations},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset}
}
```

---

## Contact

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