README.md

---
license: cc-by-4.0
task_categories:
  - robotics
  - object-detection
language:
  - en
tags:
  - physical-ai
  - manipulation
  - lerobot
  - lerobot-v3
  - lidar
  - iphone
  - arkit
  - real-world
  - household
  - cleaning
  - depth
  - hand-tracking
pretty_name: "DataSnack: Brian Does Cleaning (Yes, For Science)"
size_categories:
  - 100M<n<1B
---

# DataSnack: Brian Does Cleaning (Yes, For Science)

Real-world household manipulation data captured by an actual human — Brian — using an iPhone 15 Pro with ARKit + LiDAR. No lab. No robot arm bolted to a table. Just a guy, a can of Windex, and a Nespresso machine, captured at 1920×1440 with lossless depth frames because your VLA deserves better than a blurry YouTube video.

**Everything in this dataset is structured as [LeRobot v3](https://github.com/huggingface/lerobot).** Each session ships with a fully-formed LeRobot v3 dataset inside `transformed.zip` — `observation.state`, `observation.images.rgb`, `observation.images.depth`, `action`, subtask labels, language annotations. `load_dataset()` and go.

> **We're capturing 75+ sessions per week and pushing them here as they clear QA. Check back often — this dataset grows fast.**

This dataset is part of the [DataSnack](https://datasnack.ai) capture pipeline: iPhone → transform → LeRobot v3. The goal is scene diversity at scale. Your model has seen one kitchen a thousand times. Now it can see a different one.

---

## What's in here

3 sessions so far (more added weekly) — all from the same real home environment:

| Session | Task | Duration | Frames |
|---------|------|----------|--------|
| `6C7B49D8` | Windex (window/surface cleaning) | 40.5s | ~404 |
| `5C9E1CB4` | Windex 2 (second take) | 38.1s | ~377 |
| `31FD1797` | Nespresso (espresso machine operation) | 142.3s | ~1423 |

All sessions captured on an **iPhone 15 Pro** (LiDAR equipped), landscape-left orientation, in a domestic environment (San Mateo, CA).

---

## LeRobot v3 format

Each session's `transformed.zip` contains a complete **LeRobot v3 dataset** ready to load:

```python
from datasets import load_dataset
ds = load_dataset("datasnack/brian_does_cleaning")
```

LeRobot v3 features included per session:

| Feature | Description |
|---------|-------------|
| `observation.images.rgb` | 1920×1440 HEVC @ 30fps |
| `observation.images.depth` | 16-bit LiDAR depth @ 10fps |
| `observation.images.dynamic_mask` | Foreground mask aligned to depth frames |
| `observation.state` | 6-DoF camera pose (tx, ty, tz, qx, qy, qz, qw) |
| `observation.scene_state` | ARKit scene mesh snapshot |
| `action` | Delta pose between frames |
| `task` | Natural-language task annotation (e.g. "clean surface with Windex") |
| `subtask_labels` | Per-episode subtask breakdown |

The `lerobot_v3/` directory inside each zip follows the standard LeRobot v3 structure with `data/`, `meta/`, and `videos/` subdirectories.

---

## File structure

Each session folder contains:

```
{session_id}/
├── rgb_video.mp4           # 1920×1440 HEVC @ 30fps, full session
├── hand_tracking.json      # Per-frame hand joint positions + confidence
├── scene_mesh.ply          # Static background mesh from ARKit
├── dynamic_mesh.json       # Dynamic foreground mesh (hands + objects)
├── foreground_cloud.json   # Point cloud of dynamic foreground content
└── transformed.zip         # LeRobot v3 dataset + lossless depth + metadata:
    ├── lerobot_v3/             # ← Full LeRobot v3 dataset (load_dataset() ready)
    │   ├── data/
    │   ├── meta/
    │   └── videos/
    ├── manifest.json           # Session summary (frame count, duration)
    ├── calibration.json        # Camera intrinsics (RGB + depth), extrinsic
    ├── conventions.json        # Coordinate system, depth units, formats
    ├── timestamps.csv          # Per-frame timestamps
    └── depth_frames/
        └── *.png               # Lossless 16-bit depth @ 10fps (256×192)
```

---

## Sensor specs

| Sensor | Spec |
|--------|------|
| RGB | 1920×1440, 30fps, HEVC |
| Depth (LiDAR) | 256×192, 10fps, 16-bit PNG (millimeters) |
| Depth source | ARKit `smoothedSceneDepth` |
| IMU | 100Hz (in raw capture; not in transformed output) |
| Coordinate system | ARKit right-handed, Y-up |
| Depth–RGB alignment | Registered; extrinsic in `calibration.json` |

Depth values are lossless 16-bit unsigned integers in **millimeters**. Use `depth_frames/*.png` — the depth mp4 is 8-bit quantized and lossy.

---

## Quality

Every session passed DataSnack's real-time QA layer during capture:

| Metric | Windex | Windex 2 | Nespresso |
|--------|--------|----------|-----------|
| Tracking normal % | 100% | 100% | 100% |
| Depth in optimal range % | 100% | 100% | 100% |
| Mean depth high-conf ratio | 75.2% | 74.0% | 93.9% |
| Hand visible % | 87.4% | 86.7% | 96.1% |
| Mean ambient lux | 989 | 995 | 1006 |

No post-hoc filtering. If it's in the dataset, it passed live QA at capture time.

---

## Capture cadence

We're targeting **75+ sessions per week** across household manipulation tasks — cleaning, kitchen prep, workshop tasks, and more. Sessions are pushed here as they clear the transform + QA pipeline, typically within hours of capture.

Star or watch this repo to get notified when new sessions drop. Or just check back — there will be more.

If you need a specific task type or environment, [request it at datasnack.ai/contact](https://datasnack.ai/contact).

---

## What DataSnack is

[DataSnack](https://datasnack.ai) captures real-world manipulation data for physical AI training. The pipeline:

1. **iPhone capture** — ARKit + LiDAR, real-time QA, language-annotated episodes
2. **Transform** — depth registration, mesh reconstruction, hand tracking, LeRobot v3 packaging
3. **Publish** — LeRobot v3 native, with RLDS/TFRecord and HDF5 export also available

The thesis: scene diversity is the generalization bottleneck for VLAs. Labs produce high-rep, low-diversity data. DataSnack produces low-rep, high-diversity data — real homes, real workshops, real kitchens. Your model needs both.

---

## License

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — use it, fine-tune on it, publish results. Credit DataSnack.

---

## Citation

```bibtex
@dataset{datasnack_brian_does_cleaning_2026,
  author    = {DataSnack},
  title     = {DataSnack: Brian Does Cleaning (Yes, For Science)},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/datasnack/brian_does_cleaning}
}
```