Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
This addresses a long-standing problem in robotic manipulation for handling deformable objects, but appears incremental as it builds on existing methods like graph convolution networks and fully convolutional networks.
The paper tackles the challenge of rearranging deformable objects in robotic manipulation by proposing Graph-Transporter, a framework that uses graph-based learning and visual input to output pick-and-place actions, demonstrating effectiveness and generality in experiments.
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.