  In this paper, we present a general approach to automatically visual-servo
control the position and shape of a deformable object whose deformation
parameters are unknown. The servo-control is achieved by online learning a
model mapping between the robotic end-effector's movement and the object's
deformation measurement. The model is learned using the Gaussian Process
Regression (GPR) to deal with its highly nonlinear property, and once learned,
the model is used for predicting the required control at each time step. To
overcome GPR's high computational cost while dealing with long manipulation
sequences, we implement a fast online GPR by selectively removing uninformative
observation data from the regression process. We validate the performance of
our controller on a set of deformable object manipulation tasks and demonstrate
that our method can achieve effective and accurate servo-control for general
deformable objects with a wide variety of goal settings. Experiment videos are
available at https://sites.google.com/view/mso-fogpr
