Resources

Chef's conveyor companion box required physical hardware for every full-stack test path. To solve this, we built a hardware abstraction layer and an in-process PLC model that lets the complete deposit-and-go sequencing stack run on a developer laptop with zero hardware in the loop, allowing engineers to catch synchronization and timing bugs before they reach a live customer environment.

We’ve been teaching a physical AI system to build a complete burrito bowl with rice, corn, beans, chicken, lettuce, and cheese in under two minutes. The same Food Foundation Model architecture behind our burger assembly work has learned an entirely new skill from just over 25 hours of demonstration using regular kitchen utensils.

Ingredient onboarding is a fundamentally messy, real-world problem, but with the right combination of structured data, production feedback, and LLM-based reasoning, it becomes tractable at scale.

With the advent of physical AI and imitation learning, Chef’s AI team is developing a bi-manual physical AI system for prep table food assembly.

Modern vision-language-action models (VLAs) predict actions in chunks, but each new chunk starts from a stale observation, causing visible shaking at chunk boundaries. We traced this problem back to three compounding delay sources, measured each via system identification, and shifted the training target to compensate.

We’ve been teaching a physical AI system to pick, place, and stack a complete burger with buns, patty, cheese, lettuce, and tomato in under a minute. The technology that made this possible, our Food Manipulation Model (FFM), learned this task by demonstration with just over 26 hours of training data.

An overview of how to apply recent research breakthroughs in Generative AI to reduce customer onboarding time and improve system robustness