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. 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 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:
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.
What DataSnack is
DataSnack captures real-world manipulation data for physical AI training. The pipeline:
- iPhone capture — ARKit + LiDAR, real-time QA, language-annotated episodes
- Transform — depth registration, mesh reconstruction, hand tracking, LeRobot v3 packaging
- 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 — use it, fine-tune on it, publish results. Credit DataSnack.
Citation
@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}
}