Deformable objects present several challenges to the field of robotic manipulation. One of the tasks that best encapsulates the difficulties arising due to non-rigid behavior is shape control, which requires driving an object to a desired shape. While shape-servoing methods have been shown successful in contexts with approximately linear behavior, they can fail in tasks with more complex dynamics. We investigate an alternative approach, using offline RL to solve a planar shape control problem of a Deformable Linear Object (DLO). To evaluate the effect of material properties, two DLOs are tested namely a soft rope and an elastic cord. We frame this task as a goal-conditioned offline RL problem, and aim to learn to generalize to unseen goal shapes. Data collection and augmentation procedures are proposed to limit the amount of experimental data which needs to be collected with the real robot. We evaluate the amount of augmentation needed to achieve the best results, and test the effect of regularization through behavior cloning on the TD3+BC algorithm. Finally, we show that the proposed approach is able to outperform a shape-servoing baseline in a curvature inversion experiment.

翻译：可变形物体给机器人操作领域带来了若干挑战。最能体现非刚性行为所引发的困难之一的任务是形状控制，该任务需要将物体驱动至目标形状。尽管形状伺服方法在近似线性行为的场景中已展现出成功，但在动力学更复杂的任务中可能失效。本文探索了一种替代方案，利用离线强化学习求解可变形线性物体的平面形状控制问题。为评估材料特性的影响，我们测试了两种可变形线性物体：软绳和弹性线缆。我们将该任务构建为目标条件离线强化学习问题，旨在学习泛化到未见过的目标形状。本文提出了数据收集与增强方法，以限制需从真实机器人采集的实验数据量。我们评估了为达到最佳结果所需的数据增强程度，并通过行为克隆检验了正则化对TD3+BC算法的影响。最后，我们通过曲率反转实验证明，所提方法能够超越形状伺服基线方法。