  Deformable object manipulation has many applications such as cooking and
laundry folding in our daily lives. Manipulating elastoplastic objects such as
dough is particularly challenging because dough lacks a compact state
representation and requires contact-rich interactions. We consider the task of
flattening a piece of dough into a specific shape from RGB-D images. While the
task is seemingly intuitive for humans, there exist local optima for common
approaches such as naive trajectory optimization. We propose a novel trajectory
optimizer that optimizes through a differentiable "reset" module, transforming
a single-stage, fixed-initialization trajectory into a multistage,
multi-initialization trajectory where all stages are optimized jointly. We then
train a closed-loop policy on the demonstrations generated by our trajectory
optimizer. Our policy receives partial point clouds as input, allowing ease of
transfer from simulation to the real world. We show that our policy can perform
real-world dough manipulation, flattening a ball of dough into a target shape.
