The „Artifex Automata“ project explores the intersection of traditional craftsmenship and advanced digital technology to address the challenges of conserving historic buildings. As skilled craftspeople become increasingly scarce, this research aims to utilize robotics and machine learning to preserver and improve the craft of stonemasonry, which is critical to the restoration and adaptive reuse of heritage buildings. y integrating a machine learning model trained on the experience stonemasons into a robotic arm, the project demonstrates that this system is capable of predicting and executing precise chiseling work on sandstone that is very similar to the work of a human craftsperson.

The research focuses on developing a workflow that enables the robotic system to replicate the inricate and nuanced chiseling techniques traditionally performed by stonemasons. This is achieved thorugh a combination of motion capture, 3D scanning and machine learning, whereby the robotlearn to perform the chiseling based on data collected from a trained stonemason. The results show that while the robot can produce surfaces similar to those made by hand, there are subtle differences that challenge previous notions of craftsmanship and robotic precision. These results open up new possibilities or the integration of digital technologies in heritage conservation and represent a sustainable method of preserving historic buildings while respecting the craftsmanship that defines them.

This project, carried out as a student project in the „omputation in Architecture“ – master program at CITA, Royal Danish Academy, not only demonstrates the technical feasibility of using robots in stonemasonry, but also promotes a paradigm shift in the way craft skills can be perceived, preserved and passed pn. By bridging the gap between traditional craftsmanshop and modern technology, the project advocates a future in which digital tools and techniqoes support and preserve cultural heristage rather than replacing it. This researchhighlights the potential of human-machine collaboration and demonstrates that robotics and machine learning can play a crucial role in preserving historic craftmanshoip to ensure that these important skills are not lost over time.