论文标题
EDO-NET：从图形动力学中学习可变形对象的弹性属性
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
论文作者
论文摘要
我们研究了将概括为未知物理特性的可变形对象的学习图动力学问题。我们的关键见解是利用可以从拉动相互作用中提取的布状可变形物体的弹性物理特性的潜在表示。在本文中，我们提出了EDO-NET（弹性可变形对象 - NET），该模型是在具有不同弹性属性的各种样品上训练的图形动力学模型，这些模型不依赖于属性的地面真实标签。 EDO-NET共同学习适应模块和一个前进动力学模块。前者负责提取对象物理特性的潜在表示，而后者则利用潜在表示来预测表示为图形的布状对象的未来状态。我们在模拟和现实世界中评估了江网络，评估其功能的功能：1）概括为未知的物理属性，2）将学习的表示形式转移到新的下游任务。
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.
