  We present AdaFold, a model-based feedback-loop framework for optimizing
folding trajectories. AdaFold extracts a particle-based representation of cloth
from RGB-D images and feeds back the representation to a model predictive
control to re-plan folding trajectory at every time-step. A key component of
AdaFold that enables feedback-loop manipulation is the use of semantic
descriptors extracted from geometric features. These descriptors enhance the
particle representation of the cloth to distinguish between ambiguous point
clouds of differently folded cloths. Our experiments demonstrate AdaFold's
ability to adapt folding trajectories to cloths with varying physical
properties and generalize from simulated training to real-world execution.
