  We consider the problem of open-goal planning for robotic cloth manipulation.
Core of our system is a neural network trained as a forward model of cloth
behaviour under manipulation, with planning performed through backpropagation.
We introduce a neural network-based routine for estimating mesh representations
from voxel input, and perform planning in mesh format internally. We address
the problem of planning with incomplete domain knowledge by means of an
explicit epistemic uncertainty signal. This signal is calculated from
prediction divergence between two instances of the forward model network and
used to avoid epistemic uncertainty during planning. Finally, we introduce
logic for handling restriction of grasp points to a discrete set of candidates,
in order to accommodate graspability constraints imposed by robotic hardware.
We evaluate the system's mesh estimation, prediction, and planning ability on
simulated cloth for sequences of one to three manipulations. Comparative
experiments confirm that planning on basis of estimated meshes improves
accuracy compared to voxel-based planning, and that epistemic uncertainty
avoidance improves performance under conditions of incomplete domain knowledge.
We additionally present qualitative results on robot hardware.
