Physical interaction with textiles, such as assistive dressing, relies on advanced dextreous capabilities. The underlying complexity in textile behavior when being pulled and stretched, is due to both the yarn material properties and the textile construction technique. Today, there are no commonly adopted and annotated datasets on which the various interaction or property identification methods are assessed. One important property that affects the interaction is material elasticity that results from both the yarn material and construction technique: these two are intertwined and, if not known a-priori, almost impossible to identify through sensing commonly available on robotic platforms. We introduce Elastic Context (EC), a concept that integrates various properties that affect elastic behavior, to enable a more effective physical interaction with textiles. The definition of EC relies on stress/strain curves commonly used in textile engineering, which we reformulated for robotic applications. We employ EC using Graph Neural Network (GNN) to learn generalized elastic behaviors of textiles. Furthermore, we explore the effect the dimension of the EC has on accurate force modeling of non-linear real-world elastic behaviors, highlighting the challenges of current robotic setups to sense textile properties.

翻译：与纺织品（如辅助穿衣）的物理交互依赖于先进灵巧操作能力。纺织品在被拉伸和牵拉时表现出的潜在复杂性，既源于纱线材料属性，也源于纺织品构造技术。目前，尚无被广泛采用且带有标注的数据集来评估各类交互或属性识别方法。影响交互的一项重要属性是材料弹性，它由纱线材料和构造技术共同决定：这两者相互交织，若不具备先验知识，几乎无法通过机器人平台常用的传感手段加以识别。我们提出弹性上下文（Elastic Context, EC）这一概念，它整合了影响弹性行为的多种属性，以实现更有效的纺织品物理交互。EC的定义基于纺织工程中常用的应力/应变曲线，我们针对机器人应用对其进行了重新表述。我们利用图神经网络（GNN）来应用EC，以学习纺织品的广义弹性行为。此外，我们探讨了EC维度对非线性真实世界弹性行为精确力建模的影响，凸显了当前机器人装置在感知纺织品属性方面面临的挑战。