We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties. Our key insight is to leverage a latent representation of elastic physical properties of cloth-like deformable objects that can be extracted, for example, from a pulling interaction. In this paper we propose EDO-Net (Elastic Deformable Object - Net), a model of graph dynamics trained on a large variety of samples with different elastic properties that does not rely on ground-truth labels of the properties. EDO-Net jointly learns an adaptation module, and a forward-dynamics module. The former is responsible for extracting a latent representation of the physical properties of the object, while the latter leverages the latent representation to predict future states of cloth-like objects represented as graphs. We evaluate EDO-Net both in simulation and real world, assessing its capabilities of: 1) generalizing to unknown physical properties, 2) transferring the learned representation to new downstream tasks.

翻译：我们研究了学习可变形物体图动力学的问题，该问题能够泛化至未知的物理属性。我们的核心见解是利用布类可变形物体弹性物理属性的潜在表示，该表示可以从例如拉伸交互中提取。在本文中，我们提出了EDO-Net（弹性可变形物体网络），这是一种在图动力学上训练的模型，其训练数据涵盖大量具有不同弹性属性的样本，且不依赖于这些属性的真实标签。EDO-Net联合学习一个适应模块和一个前向动力学模块。前者负责提取物体物理属性的潜在表示，而后者则利用该潜在表示来预测以图形式表示的布类物体的未来状态。我们在仿真和现实世界中评估了EDO-Net，评估其以下能力：1）泛化至未知物理属性，2）将学习到的表示迁移到新的下游任务。