  In this work, we provide a solution for posturing the anthropomorphic
Robonaut-2 hand and arm for grasping based on visual information. A mapping
from visual features extracted from a convolutional neural network (CNN) to
grasp points is learned. We demonstrate that a CNN pre-trained for image
classification can be applied to a grasping task based on a small set of
grasping examples. Our approach takes advantage of the hierarchical nature of
the CNN by identifying features that capture the hierarchical support relations
between filters in different CNN layers and locating their 3D positions by
tracing activations backwards in the CNN. When this backward trace terminates
in the RGB-D image, important manipulable structures are thereby localized.
These features that reside in different layers of the CNN are then associated
with controllers that engage different kinematic subchains in the hand/arm
system for grasping. A grasping dataset is collected using demonstrated
hand/object relationships for Robonaut-2 to evaluate the proposed approach in
terms of the precision of the resulting preshape postures. We demonstrate that
this approach outperforms baseline approaches in cluttered scenarios on the
grasping dataset and a point cloud based approach on a grasping task using
Robonaut-2.
