README.md

---
license: cc-by-nc-4.0
task_categories:
  - robotics
  - reinforcement-learning
  - depth-estimation

tags:
  - robotics
  - manipulation
  - embodied-ai
  - imitation-learning
  - rgbd
  - realsense
  - kitchen
  - cooking
  - human-demonstration
---

# 🍳 Chinese Commercial Kitchen Manipulation Dataset — Sample Pack v0.1

> **Asia's first real commercial kitchen manipulation dataset.**  
> Professional chef (20 years) · Real restaurant environment · Multi-view RGB-D · Egocentric video

**📧 Request evaluation samples or full data:** [andy@dynamicnova.com](mailto:andy@dynamicnova.com)

---

## Overview

This sample pack contains real-world cooking demonstrations collected in an operating Chinese commercial kitchen in Zhongshan, Guangdong, China.

The data focuses on professional chef workflows rather than staged tabletop demonstrations. It includes synchronized or task-aligned multi-view video, egocentric footage, and metric depth data for evaluating robotics, embodied AI, imitation learning, and visual action understanding pipelines.

Related work such as EgoMimic suggests that egocentric human demonstration data can be valuable for scaling imitation learning, especially when paired with robot data or aligned sensing setups.

Key characteristics:

- Real commercial Chinese restaurant kitchen
- Professional chef with approximately 20 years of experience
- Egocentric, side-view, and overhead camera perspectives
- Intel RealSense D435I RGB-D capture for overhead view
- Chinese cooking tasks involving tool use, bimanual coordination, and fine-grained food manipulation

> **Note:** Large egocentric videos in this sample pack are provided as **1080p preview encodings** for easier download. Full **4K** source recordings are available upon request.

---

## Sample Pack Contents

### Task 1 — Cutting Vegetables (切菜) · 3 camera views + depth

| File | Description |
|------|-------------|
| `egocentric_1080p.mp4` | Head-mounted action camera, first-person view (1920×1080, 30fps preview) |
| `side_view.mp4` | Fixed side-view phone camera (1920×1080, 60fps, original capture) |
| `overhead/overhead.mp4` | Fixed overhead RealSense RGB (1280×720, 15fps) |
| `depth/depth.hdf5` | Aligned depth frames, float32 in meters (480×848, 2379 frames) |

### Task 2 — Wok Stir-Fry (翻炒) · 2 camera views

| File | Description |
|------|-------------|
| `egocentric_1080p.mp4` | Head-mounted action camera, first-person view (1920×1080, 30fps preview) |
| `side_view.mp4` | Fixed side-view phone camera (1920×1080, 30fps preview) |

> Depth sample is included for Task 1. Additional depth recordings may be available depending on the task and capture setup.

> *Depth note: Pixels with value 65.535m indicate no valid depth return (sensor limit). Typical valid pixel rate: ~86%.*

> Preview PNG/JPG images are included alongside the videos for quick browsing.

---

## File Structure

```
task_01_cutting/
├── egocentric_1080p.mp4
├── side_view.mp4
├── overhead/
│   ├── overhead.mp4
│   └── overhead_*.PNG
├── depth/
│   ├── depth.hdf5
│   └── check_*.jpg
├── egocentric_screenshot_*.PNG
└── side_view_*.PNG
task_02_stir_fry/
├── egocentric_1080p.mp4
├── side_view.mp4
├── egocentric_screenshot_*.PNG
└── side_view_*.PNG
```

**Read depth data (Python):**
```python
import h5py
with h5py.File("task_01_cutting/depth/depth.hdf5", "r") as f:
    depth = f["depth_meters"][:]   # (2379, 480, 848) float32, meters
    ts    = f["timestamps"][:]
```

---

## Camera Setup

### Sample pack (this repository)

| View | Task 1 | Task 2 |
|------|--------|--------|
| Egocentric | 1920×1080, 30fps (`egocentric_1080p.mp4`) | 1920×1080, 30fps (`egocentric_1080p.mp4`) |
| Side | 1920×1080, 60fps (`side_view.mp4`) | 1920×1080, 30fps (`side_view.mp4`) |
| Overhead RGB | 1280×720, 15fps | — |
| Overhead depth | 848×480 (HDF5) | — |

### Original capture specs (full dataset on request)

| View | Device | Resolution | Frame Rate |
|------|--------|------------|------------|
| Egocentric | Head-mounted action camera (DJI Osmo Action 3) | 3840×2160 (4K) | 29.97 fps |
| Side | Fixed smartphone | 1920×1080 | up to 60 fps |
| Overhead RGB-D | Intel RealSense D435I | RGB 1280×720 / Depth 848×480 | 15 fps |

---

## Collection Environment

- **Location:** Zhongshan, Guangdong, China
- **Venue:** Operating commercial Chinese restaurant
- **Operator:** Professional chef, 20 years experience
- **Consent:** Full informed consent obtained from all participants

---

## Tasks

| Task | Chinese | Difficulty | Bimanual | Camera Views | Depth |
|------|---------|-----------|----------|-------------|-------|
| Cutting vegetables | 切菜 | Medium | Partial | 3 | ✅ |
| Wok stir-fry | 翻炒 | High | ✅ | 2 | Available upon request |

**Planned in full dataset:**

| Task | Chinese | Key Challenge |
|------|---------|--------------|
| Dumpling folding | 包饺子 | High dexterity, bimanual |
| Dough kneading | 揉面 | Force estimation, rhythm |
| Deep frying | 炸 | Temperature judgment, timing |
| Pan frying | 煎 | Heat control, single/double-side flip |
| Braising / stewing | 炖煮 | Long-horizon, multi-step sequencing |
| Sauce thickening | 勾芡 | Fine motor control, timing-sensitive |
| Marinating / seasoning | 腌制/调味 | Multi-ingredient coordination |

*Action-level labels (e.g. flip timing, thickening moment, seasoning sequence) available upon request.*

---

## Access to Full Data

This public sample pack is intended for technical evaluation and early research feedback.

Additional materials may be available upon request:

- Full **4K** egocentric MP4 recordings
- Longer multi-view MP4 recordings
- Additional HDF5 metric depth sequences
- RealSense raw `.bag` recordings
- Task-level or action-level annotations
- Format conversion support, including HDF5, RLDS, or LeRobot
- Custom collection for specific kitchen workflows

---

## Contact

📧 **andy@dynamicnova.com**

Please include: tasks of interest, required volume, preferred format (HDF5 / RLDS / LeRobot), timeline.

Response within 48 hours.

---

## Citation

If you use this dataset in academic or commercial research, please cite this repository.

---

*Collected May 2026 · Zhongshan, Guangdong, China · Nova Dynamics Limited*