  Robotic fabric manipulation has applications in home robotics, textiles,
senior care and surgery. Existing fabric manipulation techniques, however, are
designed for specific tasks, making it difficult to generalize across different
but related tasks. We build upon the Visual Foresight framework to learn fabric
dynamics that can be efficiently reused to accomplish different sequential
fabric manipulation tasks with a single goal-conditioned policy. We extend our
earlier work on VisuoSpatial Foresight (VSF), which learns visual dynamics on
domain randomized RGB images and depth maps simultaneously and completely in
simulation. In this earlier work, we evaluated VSF on multi-step fabric
smoothing and folding tasks against 5 baseline methods in simulation and on the
da Vinci Research Kit (dVRK) surgical robot without any demonstrations at train
or test time. A key finding was that depth sensing significantly improves
performance: RGBD data yields an 80% improvement in fabric folding success rate
in simulation over pure RGB data. In this work, we vary 4 components of VSF,
including data generation, the choice of visual dynamics model, cost function,
and optimization procedure. Results suggest that training visual dynamics
models using longer, corner-based actions can improve the efficiency of fabric
folding by 76% and enable a physical sequential fabric folding task that VSF
could not previously perform with 90% reliability. Code, data, videos, and
supplementary material are available at
https://sites.google.com/view/fabric-vsf/.
