Self-Driving Manufacturing Labs (SDMLs) are emerging as a transformative approach to experimental manufacturing research, offering the ability to automate and optimize complex workflows with minimal human intervention. This paper defines a novel conceptual framework for SDMLs, systematically distinguishing between automation—the coordinated execution of experimental tasks through integrated hardware and software—and autonomy, the system’s ability to make data-driven decisions using machine learning and optimization algorithms. We decompose automation into four core components: materials design or manufacturing, property characterization, materials handling, and inter-machine communication. Autonomy is structured around data collection, surrogate modeling, and Bayesian optimization, enabling systems to adaptively choose optimal experimental conditions. The primary contribution of this work is the structured definition of this framework illustrated by examples, which is shown to be generalizable across different manufacturing domains, providing a modular blueprint for the design and implementation of next-generation self-driving laboratories. The paper concludes with a discussion of future directions for advancing automation, autonomy, and scaling SDMLs across broader applications in intelligent manufacturing.
Pokhrel et al. (2026) studied this question.