Adaptive Robotic Training Methods for Subtractive Manufacturing

Abstract

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