--- 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 **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} } ```