  The deformable linear objects (DLOs) are common in both industrial and
domestic applications, such as wires, cables, ropes. Because of its highly
deformable nature, it is difficult for the robot to reproduce human's dexterous
skills on DLOs. In this paper, the unknown deformation model is estimated in
both the offline and online manners. The offline learning aims to provide a
good approximation prior to the manipulation task, while the online learning
aims to compensate the errors due to insufficient training (e.g. limited
datasets) in the offline phase. The offline module works by constructing a
series of supervised neural networks (NNs), then the online module receives the
learning results directly and further updates them with the technique of
adaptive NNs. A new adaptive controller is also proposed to allow the robot to
perform manipulation tasks concurrently in the online phase. The stability of
the closed-loop system and the convergence of task errors are rigorously proved
with Lyapunov method. Simulation studies are presented to illustrate the
performance of the proposed method.
