  Robotic manipulation of cloth remains challenging for robotics due to the
complex dynamics of the cloth, lack of a low-dimensional state representation,
and self-occlusions. In contrast to previous model-based approaches that learn
a pixel-based dynamics model or a compressed latent vector dynamics, we propose
to learn a particle-based dynamics model from a partial point cloud
observation. To overcome the challenges of partial observability, we infer
which visible points are connected on the underlying cloth mesh. We then learn
a dynamics model over this visible connectivity graph. Compared to previous
learning-based approaches, our model poses strong inductive bias with its
particle based representation for learning the underlying cloth physics; it is
invariant to visual features; and the predictions can be more easily
visualized. We show that our method greatly outperforms previous
state-of-the-art model-based and model-free reinforcement learning methods in
simulation. Furthermore, we demonstrate zero-shot sim-to-real transfer where we
deploy the model trained in simulation on a Franka arm and show that the model
can successfully smooth different types of cloth from crumpled configurations.
Videos can be found on our project website.
