  Robots acting in open environments need to be able to handle novel objects.
Based on the observation that objects within a category are often similar in
their shapes and usage, we propose an approach for transferring grasping skills
from known instances to novel instances of an object category. Correspondences
between the instances are established by means of a non-rigid registration
method that combines the Coherent Point Drift approach with subspace methods.
The known object instances are modeled using a canonical shape and a
transformation which deforms it to match the instance shape. The principle axes
of variation of these deformations define a low-dimensional latent space. New
instances can be generated through interpolation and extrapolation in this
shape space. For inferring the shape parameters of an unknown instance, an
energy function expressed in terms of the latent variables is minimized. Due to
the class-level knowledge of the object, our method is able to complete novel
shapes from partial views. Control poses for generating grasping motions are
transferred efficiently to novel instances by the estimated non-rigid
transformation.
