mlsanityData to 7 Oct 2026

EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics

arXiv 2209.08996 ↗First posted 19 Sep 2022cs.CV cs.AI cs.ROcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 34 of its 46 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

34 of 46 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 2.

10 papers.

  1. 2026PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulationcited by 74
  2. 2024AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulationcited by 52
  3. 2025Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videoscited by 22
  4. 2023Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challengescited by 17
  5. 2023SculptBot: Pre-Trained Models for 3D Deformable Object Manipulationcited by 8
  6. 2025Cloth-Splatting: 3D Cloth State Estimation from RGB Supervisioncited by 7
  7. 2025Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulationscited by 2
  8. 2024Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learningcited by 1
  9. 2025Certifiably Safe Manipulation of Deformable Linear Objects via Joint Shape and Tension Predictioncited by 1
  10. 2026PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamicscited by 1