Kavli Affiliate: Xiang Zhang
| First 5 Authors: Changhao Wang, Yuyou Zhang, Xiang Zhang, Zheng Wu, Xinghao Zhu
| Summary:
Manipulating deformable linear objects by robots has a wide range of
applications, e.g., manufacturing and medical surgery. To complete such tasks,
an accurate dynamics model for predicting the deformation is critical for
robust control. In this work, we deal with this challenge by proposing a hybrid
offline-online method to learn the dynamics of cables in a robust and
data-efficient manner. In the offline phase, we adopt Graph Neural Network
(GNN) to learn the deformation dynamics purely from the simulation data. Then a
linear residual model is learned in real-time to bridge the sim-to-real gap.
The learned model is then utilized as the dynamics constraint of a trust region
based Model Predictive Controller (MPC) to calculate the optimal robot
movements. The online learning and MPC run in a closed-loop manner to robustly
accomplish the task. Finally, comparative results with existing methods are
provided to quantitatively show the effectiveness and robustness.
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