README.md

---
license: mit
task_categories:
- video-classification
- robotics
language:
- en
tags:
- egocentric
- exocentric
- first-person
- third-person
- robotics
- lerobot
- smartphone
size_categories:
- 1K<n<10K
---

# Video Subset Dataset

A HuggingFace LeRobot-compatible dataset containing **763 episodes** of egocentric and exocentric videos from indoor environments with corresponding AR pose data.

## Dataset Summary

- **Total Episodes**: 763 (Egocentric: 243, Exocentric: 520)
- **Total Frames**: 321,178
- **Frame Rate**: 30.00 FPS
- **Codebase Version**: v2.0

## Tasks

The dataset contains two distinct viewpoint tasks:

- **Task 0 (Egocentric)**: First-person view videos - 243 episodes (31.8%)
- **Task 1 (Exocentric)**: Third-person view videos - 520 episodes (68.2%)

Each episode's data includes a `task_index` field to distinguish between egocentric and exocentric videos.

## Environments

Videos were collected from 4 different indoor rooms:

| Room     | Episodes | Egocentric | Exocentric |
|----------|----------|------------|------------|
| Bedroom  | 39      | 14         | 25         |
| Sandwich | 54      | 41         | 13         |
| Laundry  | 436     | 156        | 280        |
| Bathroom | 234     | 32         | 202        |

## Dataset Structure

```
{
  "episode_index": int,
  "frame_index": int,
  "timestamp": float,
  "observation.state": List[float],  # 7D state (placeholder)
  "action": List[float],             # 7D pose: [quat_x,y,z,w, pos_x,y,z]
  "next.reward": float,
  "next.done": bool,
  "next.success": bool,
  "task_index": int,                 # 0=egocentric, 1=exocentric
  "index": int
}
```

### Action Space

The **action** field contains 7-dimensional AR pose vectors:
- **Quaternion** (4 values): Camera rotation as [x, y, z, w]
- **Position** (3 values): Camera translation as [x, y, z]

This data comes from smartphone AR tracking during video recording.

## Usage

### Load with HuggingFace Datasets

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("YOUR_USERNAME/seesawvideos-lerobot")

# Access frames
print(dataset['train'][0])
```

### Filter by Task (Egocentric vs Exocentric)

```python
# Filter egocentric only (task_index = 0)
egocentric = dataset['train'].filter(lambda x: x['task_index'] == 0)

# Filter exocentric only (task_index = 1)
exocentric = dataset['train'].filter(lambda x: x['task_index'] == 1)
```

### Using Episode Metadata

The `meta/episodes.csv` file contains detailed metadata for filtering:

```python
import pandas as pd

# Load episode metadata
episodes = pd.read_csv("meta/episodes.csv")

# Filter by task and room
ego_laundry = episodes[
    (episodes['task_label'] == 'egocentric') &
    (episodes['room'] == 'Laundry')
]

print(f"Found {len(ego_laundry)} egocentric Laundry episodes")
```

### Using Provided Utilities

```python
from utils import SeesawDatasetFilter

dataset = SeesawDatasetFilter(".")

# Get egocentric episodes
ego_episodes = dataset.get_episodes(task="egocentric")

# Filter by room and duration
bathroom_short = dataset.get_episodes(
    room="Bathroom",
    max_duration=10.0
)

# Get video paths
ego_videos = dataset.get_video_paths(task="egocentric")
```

## Files Included

- **data/chunk-000/*.parquet** - Frame-level data for all episodes
- **videos/chunk-000/observation.image/*.mp4** - Video files
- **meta/info.json** - Dataset metadata and configuration
- **meta/episodes.csv** - Per-episode metadata with labels and room info
- **meta/splits.json** - Pre-computed train/val/test splits (70/15/15)
- **utils.py** - Helper functions for filtering and loading
- **example_usage.py** - Complete usage examples
- **FILTERING_GUIDE.md** - Quick reference for filtering by task

## Statistics

### Duration by Task
- **Egocentric**: 12.5 ± 11.2 seconds (range: 1.7 - 82.2s)
- **Exocentric**: 14.7 ± 22.7 seconds (range: 1.5 - 387.6s)

### Frames by Task
- **Egocentric**: 375 ± 335 frames (range: 51 - 2,467)
- **Exocentric**: 443 ± 680 frames (range: 46 - 11,629)

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{seesawvideos_lerobot_2026,
  title={SeesawVideos LeRobot Dataset},
  author={Your Name},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/YOUR_USERNAME/seesawvideos-lerobot}}
}
```

## License

MIT License

## Additional Resources

- [LeRobot Documentation](https://github.com/huggingface/lerobot)
- [Dataset Repository](https://huggingface.co/datasets/YOUR_USERNAME/seesawvideos-lerobot)
- See `FILTERING_GUIDE.md` for detailed filtering instructions
- See `example_usage.py` for complete code examples