Cloth dynamics modeling in latent spaces and its application to robotic clothing assistance

Abstract

Real-time estimation of human-cloth relationship is crucial for efficient learning of motor skills in robotic clothing assistance. However, cloth state estimation using a depth sensor is a challenging problem with inherent ambiguity. To address this problem, we propose the offline learning of a cloth dynamics model by incorporating reliable motion capture data and applying this model for the online tracking of human-cloth relationship using a depth sensor. In this study, we evaluate the performance of using a shared Gaussian Process Latent Variable Model in learning the dynamics of clothing articles. The experimental results demonstrate the effectiveness of shared GP-LVM in capturing cloth dynamics using few data samples and the ability to generalize to unseen settings. We further demonstrate three key factors that affect the predictive performance of the trained dynamics model.