グラフニューラルネットワークを用いたケーブル操作のための変形モデルのオフライン・オンライン学習
Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks
GNNでシミュレーションからケーブルの変形ダイナミクスを学習し、実機とのギャップをオンラインで線形残差モデルにより補正、MPCと組み合わせてロバストな操作を実現した。
著者: Changhao Wang, Yuyou Zhang, Xiang Zhang, Zheng Wu, Xinghao Zhu, Shiyu Jin, Te Tang, Masayoshi Tomizuka
分類: cs.RO
原文アブストラクト
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.
