Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
- Resource Type
- Conference
- Authors
- Deng, Yuhong; Xia, Chongkun; Wang, Xueqian; Chen, Lipeng
- Source
- 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2022 IEEE International Conference on. :1910-1916 Oct, 2022
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Learning systems
Visualization
Convolution
Rubber
Task analysis
Robots
Manipulator dynamics
- Language
- ISSN
- 2577-1655
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.