  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.
