Self-Driving Labs (SDLs) integrate robotic automation with machine learning (ML) to explore chemical space. SDLs accelerate research and discovery across various disciplines, including organic synthesis, 1-3 materials chemistry, 4-6 photo- and electrochemistry. Automation liberates human researchers from time-consuming and repetitive tasks, while closed-loop optimization algorithms and/or computer vision further reduce the need for human oversight. However, the broader adoption of SDLs remains challenging because the required expertise in engineering and programming are beyond the scope of many chemistry laboratories. For example, programming an established automation system to execute a single, well-known chemical reaction can be a laborious exercise. Additionally, SDLs still require significant human involvement, skill, and time in the design of experiments and in mapping simple chemical actions to complex robot movements. The lack of standardization also poses a barrier in adaptability and transferability of SDLs development.