mlsanityData to 7 Oct 2026

Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task

arXiv 2302.10445 ↗First posted 21 Feb 2023cs.RO cs.AI cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable objects. We present a novel framework, Graph-Transporter, for goal-conditioned deformable object rearranging tasks. To tackle the challenge of complex configuration space and dynamics, we represent the configuration space of a deformable object with a graph structure and the graph features are encoded by a graph convolution network. Our framework adopts an architecture based on Fully Convolutional Network (FCN) to output pixel-wise pick-and-place actions from only visual input. Extensive experiments have been conducted to validate the effectiveness of the graph representation of deformable object configuration. The experimental results also demonstrate that our framework is effective and general in handling goal-conditioned deformable object rearranging tasks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 21 Feb 2023.

Builds on

14 of 26 references.

References · 14

  1. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  2. 2021CLIPort: What and Where Pathways for Robotic Manipulationcited by 617
  3. 2020Transporter Networks: Rearranging the Visual World for Robotic Manipulationcited by 342
  4. 2019TossingBot: Learning to Throw Arbitrary Objects with Residual Physicscited by 131
  5. 2018Sim-to-Real Reinforcement Learning for Deformable Object Manipulationcited by 124
  6. 2019Unsupervised Learning of Object Keypoints for Perception and Controlcited by 109
  7. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  8. 2020Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networkscited by 77
  9. 2019Learning to Manipulate Deformable Objects without Demonstrationscited by 76
  10. 2020Learning Predictive Representations for Deformable Objects Using Contrastive Estimationcited by 72
  11. 2019Learning Robotic Manipulation through Visual Planning and Actingcited by 39
  12. 2021SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulationcited by 24
2 more
  1. 2020Untangling Dense Knots by Learning Task-Relevant Keypointscited by 21
  2. 2020Manipulating Deformable Objects by Interleaving Prediction, Planning, and Controlcited by 8

Built on by

Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Graph learning in robotics: a surveycited by 5
  2. 2023Learning visual-based deformable object rearrangement with local graph neural networkscited by 1