The automatic shape control of deformable objects is a challenging (and currently hot) manipulation problem due to their high-dimensional geometric features and complex physical properties. In this study, a new methodology to manipulate elastic rods automatically into 2D desired shapes is presented. An efficient vision-based controller that uses a deep autoencoder network is designed to compute a compact representation of the object's infinite-dimensional shape. An online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features is used to deal with the latter's (typically unknown) mechanical properties. The proposed approach computes the rod's centerline from raw visual data in real-time by introducing an adaptive algorithm on the basis of a self-organizing network. Its effectiveness is thoroughly validated with simulations and experiments.

翻译：可变形物体的自动形状控制是一个具有挑战性(而且目前非常热)的操纵问题,原因是其高维几何特征和复杂的物理特性。本研究中介绍了一种将弹性棒自动操作成2D理想形状的新方法。一个使用深自动编码器网络的高效视觉控制器,旨在计算该物体无限维形的缩略图。一个接近该机器人配置和物体形状特征之间的感应式绘图的在线算法,用来处理后者的(通常未知的)机械特性。提议的方法通过在自我组织网络的基础上引入一个适应性算法,从实时原始视觉数据中计算棒的中线。它的有效性通过模拟和实验得到彻底验证。