  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.
