  We present a learning-based method for representing grasp poses of a high-DOF
hand using neural networks. Due to redundancy in such high-DOF grippers, there
exists a large number of equally effective grasp poses for a given target
object, making it difficult for the neural network to find consistent grasp
poses. We resolve this ambiguity by generating an augmented dataset that covers
many possible grasps for each target object and train our neural networks using
a consistency loss function to identify a one-to-one mapping from objects to
grasp poses. We further enhance the quality of neural-network-predicted grasp
poses using a collision loss function to avoid penetrations. We use an object
dataset that combines the BigBIRD Database, the KIT Database, the YCB Database,
and the Grasp Dataset to show that our method can generate high-DOF grasp poses
with higher accuracy than supervised learning baselines. The quality of the
grasp poses is on par with the groundtruth poses in the dataset. In addition,
our method is robust and can handle noisy object models such as those
constructed from multi-view depth images, allowing our method to be implemented
on a 25-DOF Shadow Hand hardware platform.
