Self-driving laboratories (SDLs) combine robotics with algorithmic decision-making to autonomously design, execute, and analyze experiments, and deftly explore the large, high-dimensional parameter spaces that confound conventional experimental approaches. This Perspective examines autonomous robotics’ roles in SDLs, challenges currently limiting generalizability and scalability, and emerging innovations in robotics could address them, thereby highlighting how the integration of advanced robotics could enable far more rapid, reproducible, and accessible experimentation across disciplines.
Moran et al. (Sat,) studied this question.