Engineering blog | Chef Robotics

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Latency-Aware Vision-Language-Action Models (VLAs) with System Identification for Real-Time Robot Control

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.

Building a General-Purpose Physical AI System for Food Manipulation

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.

Rapidly Onboarding Novel Foodstuffs with Diffusion-Based Imitation Learning

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

Latency-Aware Vision-Language-Action Models (VLAs) with System Identification for Real-Time Robot Control

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.

Building a General-Purpose Physical AI System for Food Manipulation

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.

Rapidly Onboarding Novel Foodstuffs with Diffusion-Based Imitation Learning

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

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