Learning Postural Synergies for Categorical Grasping through Shape Space Registration
This addresses the challenge of robotic grasping in on-line scenarios with partial object observation, though it appears incremental as it builds on existing human grasping taxonomies and registration techniques.
The paper tackles the problem of enabling robots to grasp novel objects by inferring grasp configurations based on object shape, using a synergy space built from human demonstrations and a categorical non-rigid registration for shape descriptors, and demonstrates this through simulation and real robot experiments with unseen objects.
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.