---
dataset_info:
- config_name: full
  features:
  - name: image
    dtype: image
  - name: actuated_angle
    struct:
    - name: '0'
      dtype: int32
    - name: '1'
      dtype: int32
    - name: '2'
      dtype: int32
    - name: '3'
      dtype: int32
    - name: '4'
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    - name: '5'
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    - name: '6'
      dtype: int32
    - name: '7'
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    - name: '8'
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    - name: '9'
      dtype: int32
    - name: '10'
      dtype: int32
    - name: '11'
      dtype: int32
    - name: '12'
      dtype: int32
    - name: '13'
      dtype: int32
    - name: '14'
      dtype: int32
    - name: '15'
      dtype: int32
  splits:
  - name: train
    num_bytes: 153291676475
    num_examples: 135236
  download_size: 153306274200
  dataset_size: 153291676475
- config_name: small
  features:
  - name: image
    dtype: image
  - name: actuated_angle
    struct:
    - name: '0'
      dtype: int32
    - name: '1'
      dtype: int32
    - name: '2'
      dtype: int32
    - name: '3'
      dtype: int32
    - name: '4'
      dtype: int32
    - name: '5'
      dtype: int32
    - name: '6'
      dtype: int32
    - name: '7'
      dtype: int32
    - name: '8'
      dtype: int32
    - name: '9'
      dtype: int32
    - name: '10'
      dtype: int32
    - name: '11'
      dtype: int32
    - name: '12'
      dtype: int32
    - name: '13'
      dtype: int32
    - name: '14'
      dtype: int32
    - name: '15'
      dtype: int32
  splits:
  - name: train
    num_bytes: 22669945934
    num_examples: 20000
  download_size: 22672135072
  dataset_size: 22669945934
configs:
- config_name: full
  data_files:
  - split: train
    path: full/train-*
- config_name: small
  data_files:
  - split: train
    path: small/train-*
  default: true
license: mit
task_categories:
- robotics
- reinforcement-learning
tags:
- robotics
- humanoid
- reinforcement
- learning
size_categories:
- 100K<n<1M
---

# 🤖 Open Humanoid Actuated Face Dataset

<p align="center">
  <img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/imgesFace.png" alt="Sample Face Image" width="400"/>
</p>

## Dataset Summary
The **Open Humanoid Actuated Face Dataset** is designed for researchers working on  
facial‑actuation control, robotics, reinforcement learning, and human–computer interaction.

* **Origin** – collected during a reinforcement‑learning (RL) training loop whose objective was to reproduce human facial expressions.
* **Platform** – a modified **i2Head InMoov** humanoid head with a silicone skin.
* **Control** – **16 actuators** driving facial features and eyeballs.
* **Pairing** – each example contains the raw RGB image **and** the exact actuator angles that produced it.

---

## Dataset Structure

| Field           | Type          | Description                                                   |
|-----------------|---------------|---------------------------------------------------------------|
| `image`         | `Image`       | RGB capture of the humanoid face (resolution **1280x720**). |
| `actuated_angle`| `struct`      | 16 integer key - values (`"0"`, `"1"` .. so on)  |


### Actuator Index Reference

| Idx | Actuator                       | Idx | Actuator                |
|:---:|--------------------------------|:---:|-------------------------|
| 00  | Cheek – Left                   | 08  | Eyelid Upper – Right    |
| 01  | Cheek – Right                  | 09  | Eyelid Lower – Right    |
| 02  | Eyeball Sideways – Left        | 10  | Forehead – Right        |
| 03  | Eyeball Up/Down – Left         | 11  | Forehead – Left         |
| 04  | Eyelid Upper – Left            | 12  | Upper Nose              |
| 05  | Eyelid Lower – Left            | 13  | Eyebrow – Right         |
| 06  | Eyeball Up/Down – Right        | 14  | Jaw                     |
| 07  | Eyeball Sideways – Right       | 15  | Eyebrow – Left          |

---

### Actuator Mapping Images

| Full‑Face Map | Eye‑Only Map |
|:-------------:|:-----------:|
| <br><img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/Screenshot%202025-05-02%20at%204.04.28%E2%80%AFPM.png" width="50%"/> | <br><img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/Screenshot%202025-05-02%20at%204.03.56%E2%80%AFPM.png" width="50%"/> |


---

## Dataset Statistics

| Split | Samples | Size |
|-------|---------|------|
| **Train (full)**  | **[135k]** | ≈ 153 GB |
| **Train (small)** | 20k | ≈ 22 GB |


---

## Usage Example

```python
from datasets import load_dataset, Image

# load the small subset
ds = load_dataset("infosys/OpenHumnoidActuatedFaceData", name="small", split="train")
ds = ds.cast_column("image", Image())   # decode image bytes ➜ PIL.Image

img = ds[0]["image"]
angles = ds[0]["actuated_angle"]        # {'0': 90, '1': 20, ...}
img.show()
print(angles)
````

> **Tip** 
> For the full corpus use `name="full"` (may require `streaming=True` once the dataset grows).

---

## Data Collection & RL Setup

A detailed description of the RL pipeline, reward design, and actuator hardware will appear in our upcoming paper (in preparation, 2025). Briefly:

1. **Vision module** extracts target expression keypoints from live human video.
2. **Policy network** predicts 16 actuator set‑points.
3. **Real‑time reward** computes expression similarity + smoothness penalties.
4. Images & angle vectors are logged every *N* steps, forming this dataset.

---

## License

Released under the **MIT License** – free for commercial and non‑commercial use.

---

## Citation

```bibtex
@misc{amirul2025openhumanoidface,
  title   = {Open Humanoid Actuated Face Dataset},
  author  = {Amirul et al.},
  year    = {2025},
  url     = {https://huggingface.co/datasets/infosys/OpenHumnoidActuatedFaceData}
}
```

---

## Contribution
1. Amirul Islam (amirul.islam@infosys.com)
2. Anant Pande (anant.pande@infosys.com)
3. Allahbaksh Asadullah (allabaksh_asadullah@infosys.com) - Mentor


## Acknowledgements

Big thanks to the Infosys and Mohammed Rafee Tarafdar (CTO, Infosys) for providing us resources to work on this research project and thanks to all the community and everyone who helped us making this possible.