  Objects within a category are often similar in their shape and usage. When
we---as humans---want to grasp something, we transfer our knowledge from past
experiences and adapt it to novel objects. In this paper, we propose a new
approach for transferring grasping skills that accumulates grasping knowledge
into a category-level canonical model. Grasping motions for novel instances of
the category are inferred from geometric deformations between the observed
instance and the canonical shape. Correspondences between the shapes are
established by means of a non-rigid registration method that combines the
Coherent Point Drift approach with subspace methods. By incorporating
category-level information into the registration, we avoid unlikely shapes and
focus on deformations actually observed within the category. Control poses for
generating grasping motions are accumulated in the canonical model from
grasping definitions of known objects. According to the estimated shape
parameters of a novel instance, the control poses are transformed towards it.
The category-level model makes our method particularly relevant for on-line
grasping, where fully-observed objects are not easily available. This is
demonstrated through experiments in which objects with occluded handles are
successfully grasped.
