Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks
Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks
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DOI：
10.1109/tits.2022.3168303
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发表时间：
2023-04
影响因子：
8.5
通讯作者：
Yadong Teng;Huimin Lu;Yujie Li;Tohru Kamiya;Y. Nakatoh;S. Serikawa;Pengxiang Gao
中科院分区：
文献类型：
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作者：
Yadong Teng;Huimin Lu;Yujie Li;Tohru Kamiya;Y. Nakatoh;S. Serikawa;Pengxiang Gao
在运输过程中，刚性物体的搬运和装载方法越来越完善。然而，无论是在当今的交通运输系统中，还是在日常生活中，如包装物体或在运输前对电缆进行分类，对可变形物体的操纵一直是不可避免的，并受到越来越多的关注。由于变形物体的超自由度和不可预测的物理状态。机器人在可变形物体的环境下完成任务比较困难。因此，我们提出了一种基于模仿学习的方法。在生成的专家演示中，智能体通过学习专家的状态序列，然后模仿专家的轨迹序列，避免了上述困难。此外，与基线方法相比，我们提出的DN-Transporter网络在涉及布料、绳索或袋子的模拟环境中更具竞争力。
In the process of transportation, the handling and loading methods of rigid objects are becoming more and more perfect. However, whether in today’s transportation system or in daily life, such as packing objects or sorting cables before transportation, the manipulation of deformable objects has been always inevitable and has attracted more and more attention. Due to the super degrees of freedom and the unpredictable physical state of deformed objects. It is difficult for robots to complete tasks under the environment of the deformable object. Therefore, we present a method based on imitation learning. In the generated expert demonstration, the agent is offered to learn the state sequence, and then imitate the expert’s trajectory sequence which avoid the above-mentioned difficulties. In addition, compared with the baseline method, our proposed DN-Transporter Networks are more competitive in a simulation environment involving cloth, ropes or bags.