The project proposes Caporciano as a training ground for robotics, in order to train those tools of the future so that they are applicable in site specific, wild, organic or vernacular architecture and uneven landscapes. Redeveloping rural areas through the use of robotics, in agriculture, caretake and building industry. The thesis is that robots based on nonlinear causality or machine learning could be trained to be versatile and work with humans to redevelop vernacular architecture, working with localities in order to keep diversification.

With the introduction of machine learning algorithms into various fields of industry, the technologies are starting to move away from forms of linear casualties towards non-linear causality. This change of approach and software knowledge has had a significant influence on the different possible applications of robotics.

Machine-learning algorithm which takes “decision” from its received data in a feedback loop creates a new way of interaction. An interaction that hopefully could create a fruitful relationship where decision making in a building process could be taken more intuitively.

Modern rhetoric within various industries, such as agriculture, medical, building industry, caretaking and production increasingly focus their aim to automonice. However, these automation processes often demand certain pre-defined spatialities, which are generally spaces, which are easy for the robots or machines to maneuver.

This project questions that tendency to sculpt the built existence to fit production methods.