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The shape control of deformable linear objects (DLOs) is challenging, sinceit is difficult to obtain the deformation models. Previous studies oftenapproximate the models in purely offline or online ways. In this paper, wepropose a scheme for the shape control of DLOs, where the unknown model isestimated with both offline and online learning. The model is formulated in alocal linear format, and approximated by a neural network (NN). First, the NNis trained offline to provide a good initial estimation of the model, which candirectly migrate to the online phase. Then, an adaptive controller is proposedto achieve the shape control tasks, in which the NN is further updated onlineto compensate for any errors in the offline model caused by insufficienttraining or changes of DLO properties. The simulation and real-worldexperiments show that the proposed method can precisely and efficientlyaccomplish the DLO shape control tasks, and adapt well to new and untrainedDLOs.

Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models