  Robotic manipulation of deformable linear objects (DLOs) has broad
application prospects in many fields. However, a key issue is to obtain the
exact deformation models (i.e., how robot motion affects DLO deformation),
which are hard to theoretically calculate and vary among different DLOs. Thus,
shape control of DLOs is challenging, especially for large deformation control
which requires global and more accurate models. In this paper, we propose a
coupled offline and online data-driven method for efficiently learning a global
deformation model, allowing for both accurate modeling through offline learning
and further updating for new DLOs via online adaptation. Specifically, the
model approximated by a neural network is first trained offline on random data,
then seamlessly migrated to the online phase, and further updated online during
actual manipulation. Several strategies are introduced to improve the model's
efficiency and generalization ability. We propose a convex-optimization-based
controller, and analyze the system's stability using the Lyapunov method.
Detailed simulations and real-world experiments demonstrate that our method can
efficiently and precisely estimate the deformation model, and achieve large
deformation control of untrained DLOs in 2D and 3D dual-arm manipulation tasks
better than the existing methods. It accomplishes all 24 tasks with different
desired shapes on different DLOs in the real world, using only simulation data
for the offline learning.

Robotics