Disruption-Resistant Deformable Object Manipulation on Basis of Online Shape Estimation and Prediction-Driven Trajectory Correction

Abstract

We consider the problem of deformable object manipulation with variable goal states and mid-manipulation disruptions. We propose an approach that integrates online shape estimation, prediction of shape transitions, and mid-manipulation trajectory correction. All functionalities are implemented using two neural network architectures. We apply this approach to the problem of cloth folding, and perform evaluation experiments in simulation and on robot hardware. We demonstrate that the system can achieve good approximation of given goal states, even when the manipulation process is disrupted by cloth slipping or external interference.