2D Mass-spring-like model for prediction of a Sponge's behaviour upon robotic interaction

Abstract

Deformable objects abound in nature, and future robots must be able to predict how they are going to behave in order to control them. In this paper we present a method capable of learning to predict the behaviour of deformable objects. We use a mass-spring-like model, which we extended to better suit our purposes, and apply it to the concrete scenario of robotic manipulation of an elastic deformable object. We describe a procedure for automatically calibrating the parameters for the model taking images and forces from a real sponge as ground truth. We use this ground truth to provide error measures that drive an evolutionary process that searches the parameter space of the model. The resulting calibrated model can make good predictions for 200 frames (6.667 seconds of real time video) even when tested with forces being applied in different positions to those trained. © Springer-Verlag London Limited 2011.