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README.md

@@ -9,6 +9,7 @@ tags:
   - physical-ai
   - manipulation
   - lerobot
+  - lerobot-v3
   - lidar
   - iphone
   - arkit
@@ -26,13 +27,17 @@ size_categories:
 
 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.
 
-This dataset is part of the [DataSnack](https://datasnack.ai) capture pipeline: iPhone → transform → training-ready. The goal is scene diversity at scale. Your model has seen one kitchen a thousand times. Now it can see a different one.
+**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, all from the same real home environment:
+3 sessions so far (more added weekly) — all from the same real home environment:
 
 | Session | Task | Duration | Frames |
 |---------|------|----------|--------|
@@ -40,7 +45,33 @@ This dataset is part of the [DataSnack](https://datasnack.ai) capture pipeline:
 | `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 the same domestic environment (San Mateo, CA).
+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.
 
 ---
 
@@ -55,7 +86,11 @@ Each session folder contains:
 ├── 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         # Everything above + depth frames + metadata:
+└── 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
@@ -77,7 +112,7 @@ Each session folder contains:
 | 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**. The `depth_video_format` field in `conventions.json` describes the lossy quantized mp4 variant (not included here — use the PNGs).
+Depth values are lossless 16-bit unsigned integers in **millimeters**. Use `depth_frames/*.png` — the depth mp4 is 8-bit quantized and lossy.
 
 ---
 
@@ -97,18 +132,26 @@ 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, format conversion
-3. **Publish** — LeRobot v3, RLDS/TFRecord, HDF5, or raw assets
+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.
 
-More sessions are added continuously. If you need a specific task type or environment, [request it](https://datasnack.ai/contact).
-
 ---
 
 ## License