We conducted a simple synthesis experiment of polyamic acid particles using a handmade robotic device and a multimodal foundation model. The robotic mechanism allowed for control and recording of the experimental process, after which the multimodal AI appropriately analyzed the automatically generated information, demonstrating the ability to output content akin to an electronic lab notebook. The high level of abstraction provided new pathways for low-cost and comprehensive input and output of process information, observational records, and measurement results. Moreover, the foundation model could also compare the appearance of reaction solutions based on image data and understand the misalignment of the sample bottle's position to analyze why the experiments were unsuccessful. In the long term, a lab automation system combining optimized hardware with cutting-edge AI is expected to provide benefits such as accumulating tacit and experiential knowledge that was difficult to store as objective data. We are advancing methodologies to enable robots to explicitly control operations by capturing and codifying laboratory expertise and implicit knowledge. Our approach centers on leveraging the multimodal capabilities of large-scale language models, with the goal of achieving comprehensive and autonomous system control through next-generation AI. We are conducting a polycondensation reaction using pyromellitic dianhydride (PMDA) and 4,4'-oxydianiline (ODA).