We present an autonomous chemical synthesis robot for the exploration, discovery, and optimisation of nanostructures driven by real-time spectroscopic feedback, theory and machine learning algorithms that control the reaction conditions and allow the selective templating of reactions. This approach allows the transfer of materials as seeds between cycles of exploration, opening the search space like gene transfer in biology. The open-ended exploration of the seed-mediated multistep synthesis of gold nanoparticles (AuNPs) via in-line UV-Vis characterisation led to the discovery of five categories of nanoparticles by only performing ca. one thousand experiments in three hierarchically-linked chemical spaces. The platform optimised nanostructures with desired optical properties by combining experiments and extinction spectrum simulations to achieve a yield of up to 95%. The synthetic procedure is outputted in a universal format using Chemical Description Language (χDL) with analytical data to produce a unique digital signature to enable the reproducibility of the synthesis.