Learning computer-aided manufacturing from demonstration: a case study with probabilistic movement primitives in robot wood carving

Computer-Aided Manufacturing (CAM) tools are a key component in many digital fabrication workflows, translating digital designs into machine instructions to manufacture physical objects. However, conventional CAM tools are tailored for standard manufacturing processes such as milling, turning or laser cutting, and can therefore be a limiting factor - especially for craftspeople and makers who want to employ non-standard, craft-like operations. Formalizing the tacit knowledge behind such operations to incorporate it in new CAM-routines is inherently difficult and often not feasible for the ad hoc incorporation of custom manufacturing operations in a digital fabrication workflow. In this paper, we address this gap by exploring the integration of Learning from Demonstration (LfD) into digital fabrication workflows, allowing makers to establish new manufacturing operations by providing manual demonstrations. To this end, we perform a case study on robot wood carving with hand tools, in which we integrate probabilistic movement primitives (ProMPs) into Rhino's Grasshopper environment to achieve basic CAM-like functionality. Human demonstrations of different wood carving cuts are recorded via kinesthetic teaching and modeled by a mixture of ProMPs to capture correlations between the toolpath parameters. The ProMP model is then exposed in Grasshopper, where it functions as a translator from drawing input to toolpath output. With our pipeline, makers can create simplified 2D drawings of their carving patterns with common CAD tools and then seamlessly generate skill-informed 6 degree-of-freedom carving toolpaths from them, all in the same familiar CAD environment. We demonstrate our pipeline on multiple wood carving applications and discuss its limitations, including the need for iterative toolpath adjustments to address inaccuracies. Our findings illustrate the potential of LfD in augmenting CAM tools for specialized and highly customized manufacturing tasks. At the same time, the question of how to best represent carving skills for flexible and generalizable toolpath generation remains open and requires further investigation.