  This paper addresses the problem of robotic cutting during disassembly of
products for materials separation and recycling. Waste handling applications
differ from milling in manufacturing processes, as they engender considerable
variety and uncertainty in the parameters (e.g. hardness) of materials which
the robot must cut. To address this challenge, we propose a learning-based
approach incorporating elements of interaction control, in which the robot can
adapt key parameters, such as feed rate, depth of cut, and mechanical
compliance during task execution. We show how a mathematical model of cutting
mechanics, embedded in a simulation environment, can be used to rapidly train
the system without needing large amounts of data from physical cutting trials.
The simulation approach was validated on a real robot setup based on four case
study materials with varying structural and mechanical properties. We
demonstrate the proposed method minimises process force and path deviations to
a level similar to offline optimal planning methods, while the average time to
complete a cutting task is within 25% of the optimum, at the expense of reduced
volume of material removed per pass. A key advantage of our approach over
similar works is that no prior knowledge about the material is required.
