The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.

翻译：可变形线性物体（DLO）的机器人操作是一项重要且具有挑战性的任务，在实际应用中具有关键意义。针对该问题的传统基于模型的方法需要精确的模型来捕捉机器人运动对DLO形变的影响。当前，数据驱动模型在质量与计算时间之间实现了最佳平衡。本文分析了多种基于学习的DLO三维模型，并提出了一种基于Transformer架构的新模型。得益于所提出的缩放方法，该模型即使对不同长度的DLO也能实现卓越的精度。此外，我们引入了一种数据增强技术，该技术几乎能提升所有被考虑的DLO数据驱动模型的预测性能。借助这一技术，即使是简单的多层感知器（MLP）也能在显著加快评估速度的同时达到接近最优水平的性能。在实验中，我们通过多个具有挑战性的数据集定量比较了基于学习的DLO三维模型的性能，并展示了其在DLO成形任务中的适用性。