Given this need, recent research efforts have shown remarkable progress in automated experimentation for various steps of the materials discovery cycle, including synthesis, formulation and device fabrication, functional characterization, and computational simulations. Notable advances in this regard range from early examples of automated biomolecule synthesis, flow chemistry and microfluidics for solution-phase synthesis , high-throughput experimentation for biological assays or reaction screening, to automated systems for fabricating solid-state or thin-film materials, as well as automated computational tools and the application of big data for virtual screening. The merger of such automation platforms with advances from "Artificial Intelligence" (AI) has given rise to the concept of "self-driving laboratories" (SDLs): the closed-loop integration of data-driven experiment planning with automated experiment execution. Such autonomous experimentation systems have been successfully demonstrated to address diverse optimization problems in a sample-efficient (and thereby time-efficient) manner, significantly reducing the required experimental resources. While early examples have mainly focused on reaction condition optimization, recent SDLs have shown the potential to discover new materials compositions, e.g. for quantum dot synthesis, thin-film devices, nanoparticles or solid-state materials. Whereas automated systems have enabled increased throughput and enhanced reproducibility compared to human experimentation, their flexibility and reconfigurability has remained limited. Whilst this can be partly attributed to the often human-centric hardware design, software integration and the dynamic orchestration of automated workflows have remained a major challenge in automated laboratories. To address this challenge, a range of software solutions such as ChemOS,[18] AresOS,[19] NIMS-OS,[20] among others, have been proposed for workflow management and hardware integration, with more ambitious approaches implementing complete frameworks combining hardware and software, and standardizing experimental protocols. However, existing frameworks overlook how researchers automate hardware, and do not provide flexible human-robotic integrations or a proper universal framework. Moreover, current frameworks do not incorporate IoT breakthroughs like fog computing, which offer strong data management and storage without relying on remote servers. Failing to embrace these cutting-edge technologies is a missed opportunity for optimizing SDL performance and making them more usable for experimental scientists.