This paper addresses the problem of contact-based manipulation of deformable linear objects (DLOs) towards desired shapes with a dual-arm robotic system. To alleviate the burden of high-dimensional continuous state-action spaces, we model the DLO as a kinematic multibody system via our proposed keypoint detection network. This new perception network is trained on a synthetic labeled image dataset and transferred to real manipulation scenarios without conducting any manual annotations. Our goal-conditioned policy can efficiently learn to rearrange the configuration of the DLO based on the detected keypoints. The proposed hierarchical action framework tackles the manipulation problem in a coarse-to-fine manner (with high-level task planning and low-level motion control) by leveraging on two action primitives. The identification of deformation properties is avoided since the algorithm replans its motion after each bimanual execution. The conducted experimental results reveal that our method achieves high performance in state representation of the DLO, and is robust to uncertain environmental constraints.

翻译：本文探讨以接触为基础操纵变形线性物体(DLOs),使之以双臂机器人系统为理想形状的问题。为减轻高维连续状态行动空间的负担,我们通过我们拟议的关键点探测网络,将DLO模拟为动态多体系统。这个新的认知网络在合成标签图像数据集上接受培训,并在不做任何手动说明的情况下转移到实际操作情景。我们的目标条件政策可以有效地学习根据所发现的关键点重新安排DLO的配置。拟议的等级行动框架通过利用两种原始动作(高任务规划和低级别运动控制),以粗略到平坦的方式处理操纵问题。由于算法在每次双人执行后重新规划其动作,所以避免了对变形特性的识别。进行实验的结果表明,我们的方法在DLO的州代表中取得了很高的性能,并且能够应对不确定的环境制约因素。