Lab-in-the-Loop
An AI architecture pattern where robotic lab systems run physical experiments and feed results directly back into the AI's decision-making process.
Lab-in-the-loop integration connects AI hypothesis generation with automated experimental execution. Rather than stopping at predictions, these systems trigger robotic synthesis, collect real-world data, and use experimental outcomes to refine subsequent iterations. This closes the gap between digital simulation and physical validation, enabling 10-100× throughput gains by sampling less than 1% of parameter spaces through active learning.
Also known as
lab in the loop, closed-loop lab, autonomous lab integration