Title: Learning Postural Synergies for Categorical Grasping through Shape Space Registration,
Abstract: Every time a person encounters an object with a given degree of familiarity,
he/she immediately knows how to grasp it. Adaptation of the movement of the
hand according to the object geometry happens effortlessly because of the
accumulated knowledge of previous experiences grasping similar objects. In this
paper, we present a novel method for inferring grasp configurations based on
the object shape. Grasping knowledge is gathered in a synergy space of the
robotic hand built by following a human grasping taxonomy. The synergy space is
constructed through human demonstrations employing a exoskeleton that provides
force feedback, which provides the advantage of evaluating the quality of the
grasp. The shape descriptor is obtained by means of a categorical non-rigid
registration that encodes typical intra-class variations. This approach is
especially suitable for on-line scenarios where only a portion of the object's
surface is observable. This method is demonstrated through simulation and real
robot experiments by grasping objects never seen before by the robot.