  Robotic manipulation of deformable objects is a difficult problem especially
because of the complexity of the many different ways an object can deform.
Searching such a high dimensional state space makes it difficult to recognize,
track, and manipulate deformable objects. In this paper, we introduce a
predictive, model-driven approach to address this challenge, using a
pre-computed, simulated database of deformable object models. Mesh models of
common deformable garments are simulated with the garments picked up in
multiple different poses under gravity, and stored in a database for fast and
efficient retrieval. To validate this approach, we developed a comprehensive
pipeline for manipulating clothing as in a typical laundry task. First, the
database is used for category and pose estimation for a garment in an arbitrary
position. A fully featured 3D model of the garment is constructed in real-time
and volumetric features are then used to obtain the most similar model in the
database to predict the object category and pose. Second, the database can
significantly benefit the manipulation of deformable objects via non-rigid
registration, providing accurate correspondences between the reconstructed
object model and the database models. Third, the accurate model simulation can
also be used to optimize the trajectories for manipulation of deformable
objects, such as the folding of garments. Extensive experimental results are
shown for the tasks above using a variety of different clothing.
