For warehouse robots, breaking a glass bottle is expensive. DEVA-3 allows robots to "simulate" a grasp in their head before moving a muscle. If the simulation shows the object slipping, the robot adjusts its grip pressure. This reduces real-world trial-and-error by 90%.
The car that avoids the accident, the robot that doesn't drop the egg, and the drone that navigates the forest—they will all be running something very close to DEVA-3 by 2027. deva-3
We have tried rule-based systems (they break in the real world), end-to-end deep learning (they hallucinate), and large language models (they lack physics). But a new architecture is emerging from the labs that might finally crack the code. For warehouse robots, breaking a glass bottle is expensive
They trained DEVA-3 on nothing but dashcam footage from Phoenix, Arizona. Then, they gave it a single frame from a snowy street in Oslo—something it had never seen. This reduces real-world trial-and-error by 90%
Published by: The AI Frontier Reading Time: 6 minutes
If you haven’t heard of it yet, you will. DEVA—which stands for —is a family of models designed to understand the world not as a series of static images, but as a continuous, interactive simulation. Version 3 is where it gets scary good. What is DEVA-3? In simple terms, DEVA-3 is a World Model . Unlike a Large Language Model (LLM) that predicts the next word, or a diffusion model that predicts the next pixel, DEVA-3 predicts the next state of reality .
The model hallucinated cars sliding, pedestrians walking cautiously, and brake lights flashing. It had never seen snow, but it had learned friction and low-traction behavior from dry roads. It generalized the concept of slipperiness.