  The shape control of deformable linear objects (DLOs) is challenging, since
it is difficult to obtain the deformation models. Previous studies often
approximate the models in purely offline or online ways. In this paper, we
propose a scheme for the shape control of DLOs, where the unknown model is
estimated with both offline and online learning. The model is formulated in a
local linear format, and approximated by a neural network (NN). First, the NN
is trained offline to provide a good initial estimation of the model, which can
directly migrate to the online phase. Then, an adaptive controller is proposed
to achieve the shape control tasks, in which the NN is further updated online
to compensate for any errors in the offline model caused by insufficient
training or changes of DLO properties. The simulation and real-world
experiments show that the proposed method can precisely and efficiently
accomplish the DLO shape control tasks, and adapt well to new and untrained
DLOs.
