We study the problem of learning graph dynamics of deformable objects which generalize to unknown physical properties. In particular, we leverage a latent representation of elastic physical properties of cloth-like deformable objects which we explore through a pulling interaction. We propose EDO-Net (Elastic Deformable Object - Net), a model trained in a self-supervised fashion on a large variety of samples with different elastic properties. EDO-Net jointly learns an adaptation module, responsible for extracting a latent representation of the physical properties of the object, and a forward-dynamics module, which 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 of cloth-like deformable objects, 2) transferring the learned representation to new downstream tasks.

翻译：我们研究如何学习可变形物体的图解动态,将其概括为未知的物理属性。特别是,我们利用我们通过拉动互动探索的布状变形物体的弹性物理属性的潜在代表。我们提议EDO-Net(Elatic Deformable objects-Net),这是一个以自我监督方式对具有不同弹性属性的大量样本进行自我监督培训的模型。EDO-Net共同学习一个适应模块,负责提取该物体物理属性的潜在代表,以及一个前方动力模块,该模块利用这些潜在代表来预测以图表形式表示的类似布状物体的未来状态。我们在模拟和现实世界中评价EDO-Net,评估其能力:(1) 概括到布状变形物体的未知物理属性,(2) 将所学到的表述转移到新的下游任务。