  Prosthetic hands can help people with limb difference to return to their life
routines. Commercial prostheses, however have several limitations in providing
an acceptable dexterity. We approach these limitations by augmenting the
prosthetic hands with an off-the-shelf depth sensor to enable the prosthesis to
see the object's depth, record a single view (2.5-D) snapshot, and estimate an
appropriate grasp type; using a deep network architecture based on 3D point
clouds called PointNet. The human can act as the supervisor throughout the
procedure by accepting or refusing the suggested grasp type. We achieved the
grasp classification accuracy of up to 88%. Contrary to the case of the RGB
data, the depth data provides all the necessary object shape information, which
is required for grasp recognition. The PointNet not only enables using 3-D data
in practice, but it also prevents excessive computations. Augmentation of the
prosthetic hands with such a semi-autonomous system can lead to better
differentiation of grasp types, less burden on user, and better performance.
