  Learning-based approaches to grasp planning are preferred over analytical
methods due to their ability to better generalize to new, partially observed
objects. However, data collection remains one of the biggest bottlenecks for
grasp learning methods, particularly for multi-fingered hands. The relatively
high dimensional configuration space of the hands coupled with the diversity of
objects common in daily life requires a significant number of samples to
produce robust and confident grasp success classifiers. In this paper, we
present the first active deep learning approach to grasping that searches over
the grasp configuration space and classifier confidence in a unified manner. We
base our approach on recent success in planning multi-fingered grasps as
probabilistic inference with a learned neural network likelihood function. We
embed this within a multi-armed bandit formulation of sample selection. We show
that our active grasp learning approach uses fewer training samples to produce
grasp success rates comparable with the passive supervised learning method
trained with grasping data generated by an analytical planner. We additionally
show that grasps generated by the active learner have greater qualitative and
quantitative diversity in shape.
