Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
arXiv:2302.10445 · doi:10.1109/SMC53654.2022.9945180
Abstract
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.
arXiv commenthas been accepted by IEEE International Conference on Systems, Man and Cybernetics 2022
References in corpus (5)
- Unsupervised Learning of Object Keypoints for Perception and Control
- CLIPort: What and Where Pathways for Robotic Manipulation
- Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
- Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
- Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control