Feb. 8, 2024, 5:47 a.m. | Alberta Longhini Marco Moletta Alfredo Reichlin Michael C. Welle David Held Zackory Erickson Danica Kr

cs.CV updates on arXiv.org arxiv.org

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 …

cs.ai cs.cv cs.ro dynamics elastic example graph insight key objects paper representation study

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