Adaptive Robotic Training Methods for Subtractive Manufacturing

This paper presents the initial developments of a method to train an adaptive robotic system for
subtractive manufacturing with timber, based on sensor feedback, machine-learning procedures and
material explorations. The methods were evaluated in a series of tests where the trained networks
were successfully used to predict fabrication parameters for simple cutting operations with chisels
and gouges. The results suggest potential benefits for non-standard fabrication methods and a
more effective use of material affordances.