  Rearranging and manipulating deformable objects such as cables, fabrics, and
bags is a long-standing challenge in robotic manipulation. The complex dynamics
and high-dimensional configuration spaces of deformables, compared to rigid
objects, make manipulation difficult not only for multi-step planning, but even
for goal specification. Goals cannot be as easily specified as rigid object
poses, and may involve complex relative spatial relations such as "place the
item inside the bag". In this work, we develop a suite of simulated benchmarks
with 1D, 2D, and 3D deformable structures, including tasks that involve
image-based goal-conditioning and multi-step deformable manipulation. We
propose embedding goal-conditioning into Transporter Networks, a recently
proposed model architecture for learning robotic manipulation that rearranges
deep features to infer displacements that can represent pick and place actions.
In simulation and in physical experiments, we demonstrate that goal-conditioned
Transporter Networks enable agents to manipulate deformable structures into
flexibly specified configurations without test-time visual anchors for target
locations. We also significantly extend prior results using Transporter
Networks for manipulating deformable objects by testing on tasks with 2D and 3D
deformables. Supplementary material is available at
https://berkeleyautomation.github.io/bags/.
