6,000+ episodes · Food state changes · Service protocol processes · Food safety compliance · Catering service robots
| Dataset Name | Restaurant Service and Kitchen Operations Dataset |
| Total Data Volume | 6,000+ episodes |
| Data Type | Videos, food status labels, service flow steps, food safety compliance markers, temperature indicators |
| Video Specifications | 1920×1080 @ 30 FPS |
| Delivery Format | LeRobot 3.0 / RLDS / HDF5 / JSON Metadata |
| License Type | Licensed Data or Custom Projects |
Each piece of data contains complete structured fields, covering all requirements from basic metadata to professional analysis.
| Field Category | Typical Fields |
|---|---|
| Front-of-house and back-of-house scene video | Video, front/back kitchen area, customer traffic, table status, camera perspective, workspace number |
| Food State Changes | Food form, cooking phase, plating results, temperature indicators, allergens, raw material markers, finished-dish images, quality ratings |
| Service Flow Labels | Timestamps and protocol ratings, customer interaction types, greeting guests, ordering, serving food, patrolling, billing, tableware, utensils, etc. |
| Safety and Compliance | Masks / gloves, raw-cooked separation, cleaning and sterilization, waste sorting, food preservation, HACCP critical control points |
| Metadata and Compliance | Dining area code, collection time, scope of authorization, food safety manager review, privacy de-identification markers |
The following is a de-identified structured JSON sample illustrating the typical data format.
{ "episode_id": "RES-2026-0011234", "restaurant": "fast_casual_05", "area": "kitchen", "video": {"rgb": "rgb_0011234.mp4", "depth": "depth_0011234.mkv", "fps": 30}, "food_states": [ {"time": 0, "state": "raw", "item": "beef_patty"}, {"time": 180, "state": "grilled", "item": "beef_patty", "temp_c": 72} ], "service_steps": [ {"time": 245, "action": "plate", "dish": "burger_set", "present_score": 8.5} ], "safety": {"mask": true, "gloves": true, "cross_contamination": false}, "compliance": {"food_sampled": true, "waste_sorted": true}
}Training for front-of-house service robots such as food delivery, snack service, and bussing.
Food state change data is used to train cooking monitoring and fire alarm systems.
Real-time visual detection of masks, gloves, biological contamination, and cleaning and disinfection.
Training of service flow data for guest, catering, and station monitoring assessment models.
Interactive data on customers and food for training personalized recommendation and nutrition matching models.
Food consumption and food preparation action data are optimized after training on cooking stock and scheduling.
Changsha Langhui Information Technology Co., Ltd. The DataAssetsAPI platform is dedicated to providing high-quality, compliant data assets for AI companies and research institutions. It supports flexible combination by dimension and by quantity scale.
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