  The ability to successfully grasp objects is crucial in robotics, as it
enables several interactive downstream applications. To this end, most
approaches either compute the full 6D pose for the object of interest or learn
to predict a set of grasping points. While the former approaches do not scale
well to multiple object instances or classes yet, the latter require large
annotated datasets and are hampered by their poor generalization capabilities
to new geometries. To overcome these shortcomings, we propose to teach a robot
how to grasp an object with a simple and short human demonstration. Hence, our
approach neither requires many annotated images nor is it restricted to a
specific geometry. We first present a small sequence of RGB-D images displaying
a human-object interaction. This sequence is then leveraged to build associated
hand and object meshes that represent the depicted interaction. Subsequently,
we complete missing parts of the reconstructed object shape and estimate the
relative transformation between the reconstruction and the visible object in
the scene. Finally, we transfer the a-priori knowledge from the relative pose
between object and human hand with the estimate of the current object pose in
the scene into necessary grasping instructions for the robot. Exhaustive
evaluations with Toyota's Human Support Robot (HSR) in real and synthetic
environments demonstrate the applicability of our proposed methodology and its
advantage in comparison to previous approaches.
