Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models
Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models
复制标题
通过局部线性变形模型的离线和在线学习对可变形线性物体进行形状控制
DOI：
10.1109/icra46639.2022.9812244
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Xiang Li
中科院分区：
文献类型：
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作者：
Mingrui Yu;Hanzhong Zhong;Xiang Li
可变形线状物体的形状控制是一个具有挑战性的问题，因为变形模型的获取是非常困难的。以前的研究往往是以纯粹的离线或在线方式近似模型。在本文中，我们提出了一个计划的形状控制的DLO，未知模型估计离线和在线学习。该模型是制定在一个局部线性格式，并近似的神经网络（NN）。首先，离线训练NN以提供模型的良好初始估计，其可以直接迁移到在线阶段。然后，提出了一种自适应控制器来实现板形控制任务，其中神经网络进一步在线更新，以补偿离线模型中由于训练不足或DLO特性变化而引起的任何误差。仿真和实际实验表明，该方法能够准确、高效地完成DLO板形控制任务，并能很好地适应新的和未经训练的DLO。
The shape control of deformable linear objects (DLOs) is challenging, since it is difficult to obtain the deformation models. Previous studies often approximate the models in purely offline or online ways. In this paper, we propose a scheme for the shape control of DLOs, where the unknown model is estimated with both offline and online learning. The model is formulated in a local linear format, and approximated by a neural network (NN). First, the NN is trained offline to provide a good initial estimation of the model, which can directly migrate to the online phase. Then, an adaptive controller is proposed to achieve the shape control tasks, in which the NN is further updated online to compensate for any errors in the offline model caused by insufficient training or changes of DLO properties. The simulation and real-world experiments show that the proposed method can precisely and efficiently accomplish the DLO shape control tasks, and adapt well to new and untrained DLOs.
DOI：
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发表时间：
2020-11
期刊：
ArXiv
影响因子：
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作者：
Xingyu Lin;Yufei Wang;Jake Olkin;David Held
通讯作者：
Xingyu Lin;Yufei Wang;Jake Olkin;David Held
使用局部线性潜在动力学的可变形线性对象预测
DOI：
10.1109/icra48506.2021.9560955
发表时间：
2021
期刊：
2021 IEEE International Conference on Robotics and Automation (ICRA
影响因子：
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作者：
Zhang, Wenbo;Schmeckpeper, Karl;Chaudhari, Pratik;Daniilidis, Kostas
通讯作者：
Daniilidis, Kostas