Despite the advancements in the commercial automation of metallography instruments, the process of metallography sample preparation is still predominantly semi-automated. Individual subprocesses—from raw material sectioning, through mechanical grinding and polishing, to final specimen contrasting—are automated in isolation. This approach offers flexibility in the preparation workflow but falls short of exploiting current automation capabilities. Modern automation technologies, including collaborative and mobile robotics, artificial intelligence-based computer vision, and advanced sensorics, could enable true holistic automation of metallographic sample preparation. However, the diversity inherent in sample preparation processes necessitates the continued involvement of skilled operators. Integrating user-friendly interfaces of collaborative robot (cobot) programming could foster a synergistic workspace, combining holistic automation with human creativity to address the complexities of metallographic specimen preparation. This could not only aid in enhancing operator safety and minimizing repetitive tasks but also facilitate the 3D study of materials and the documentation of each specimen's preparation process for easier future replication, advancing towards FAIR data principles and open science. There are a few examples of advanced solutions that integrate multiple specimen preparation steps, but the purchase price seems to prevent the widespread adoption of high-level automation, as the industrial-grade robotic solutions are rarely affordable. Recognizing that a holistic solution is yet to be achieved, our study has investigated the application of versatile, cost-effective, and user-friendly mini robots in key phases of the sample preparation process, marking crucial steps towards achieving complex robotization. The results and insights from these experiments will be detailed in this work.