Fig. 3: The cycle of challenges forestalling the advancement of self-driving laboratory (SDL) technologies.
From: Science acceleration and accessibility with self-driving labs
Incomplete integration of artificial intelligence (AI), hardware, and software prevents the creation of agile experimental systems and the complexity of these systems renders SDLs into black-box systems, which can struggle to balance all the requests and questions given to them. Questions and requests to different aspects of SDLs receive unclear answers and this incomplete information results in difficulties collaborating with other scientists and industry partners, which in turn impacts funding and support. A lack of cohesive support challenges the advancement of human–AI–robot collaboration and the improvement of SDL technologies, starting the cycle anew.
