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

arXiv: https://arxiv.org/abs/2302.10445 · HTML: https://mlsanity.com/paper/2302.10445/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [TossingBot: Learning to Throw Arbitrary Objects with Residual Physics](https://mlsanity.com/paper/1903.11239/) (2019)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [Unsupervised Learning of Object Keypoints for Perception and Control](https://mlsanity.com/paper/1906.11883/) (2019)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [Learning to Manipulate Deformable Objects without Demonstrations](https://mlsanity.com/paper/1910.13439/) (2019)
- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [Learning Robotic Manipulation through Visual Planning and Acting](https://mlsanity.com/paper/1905.04411/) (2019)
- [SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation](https://mlsanity.com/paper/2101.11812/) (2021)
- [Untangling Dense Knots by Learning Task-Relevant Keypoints](https://mlsanity.com/paper/2011.04999/) (2020)
- [Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control](https://mlsanity.com/paper/2001.09950/) (2020)

## Built on by (most cited first, 2 of 2)

- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [Learning visual-based deformable object rearrangement with local graph neural networks](https://mlsanity.com/paper/2310.10307/) (2023)

## 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.