.png){kind=icon}
返回
Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2022.3168303.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Task analysis
Robots
Robot kinematics
Visualization
Deformable models
Transportation
Computational modeling
Deformable objects
imitation learning
manipulation planning
robot vision
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容