Abstract
Movement generation, and especially generalisation to unseen situations, plays an important role in robotics. Different types of movement generation methods exist such as spline based methods, dynamical system based methods, and methods based on Gaussian mixture models (GMMs). Using a large, new dataset on human manipulations, in this paper we provide a highly detailed comparison of three most widely used movement encoding and generation frameworks: dynamic movement primitives (DMPs), time based Gaussian mixture regression (tbGMR) and stable estimator of dynamical systems (SEDS). We compare these frameworks with respect to their movement encoding efficiency, reconstruction accuracy, and movement generalisation capabilities. The new dataset consists of nine object manipulation actions performed by 12 humans: pick and place, put on top/take down, put inside/take out, hide/uncover, and push/pull with a total of 7,652 movement examples. Our analysis shows that for movement encoding and reconstruction DMPs are the most efficient framework with respect to the number of parameters and reconstruction accuracy if a sufficient number of kernels is used. In case of movement generalisation to new start- and end-point situations, DMPs and task parameterized GMM (TP-GMM, movement generalisation framework based on tbGMR) lead to similar performance and outperform SEDS. Furthermore we observe that TP-GMM and SEDS suffer from inaccurate convergence to the end-point as compared to DMPs. These different quantitative results will help designing trajectory representations in an improved task-dependent way in future robotic applications.
Abstract (translated)
URL
https://arxiv.org/abs/2211.13024
