"Trust is good, control is better." This apocryphal quote, often attributed to Lenin, might be interpreted as a guiding principle for good research data management (RDM) practices. The "trust" aspect of RDM is codified in guidelines for RDM, and implemented by overarching standards as well as grassroots approaches, such as small data. However, the "control" aspect of RDM has a large rift between the computational and experimental domains. Researchers in computational materials science tend to focus on implementing code interoperability and developing workflow management tools. On the other hand, experimental researchers are (still) tackling digitalisation, mainly via electronic lab notebooks and instrument automation. chestrators or workflow manager softwares (WFMSs) is, in our view, crucial. In a previous work, Stricker et al. demonstrated a proof-of-concept control of a bespoke experimental set-up using an established computational WFMS pyiron. In this work, we present how our autonomous robotic battery materials research platform, Aurora, has been integrated with the WFMS AiiDA, originally designed for computational science. In contrast with the previous works discussed above, here a WFMS is taught to interact with an offthe-shelf potentiostat using a shim around vendor-supplied libraries, exploiting existing plugin architectures. Such integration enables the coupling of experiments with computational workflows using digital twins. The ultimate goal for the Aurora platform, developed as part of the Battery Interface Genome -Materials Acceleration Platform (BIG-MAP), is to link physics-and data-based modelling with automated orchestration of battery assembly and battery cycling experiments, forming a closed-loop sequence. Additionally, we discuss the development of user interfaces that are familiar to experimentalists but compatible with computational tools, implemented with the graphical web platform AiiDAlab, based on AiiDA. Finally, we discuss new features incorporated into AiiDA, that allow for an automated supervision of experimental (and computational) tasks during their runtime. In the following Implementation section, we first outline the project components and their design philosophy. In particular, we focus on the elements of our software stack developed as part of this work. In the Results section, we present a case study using a range of coin cell batteries built by an automated coin cell assembly robot, illustrating the user experience and outlining the applications of our work.