  This paper concerns the problem of how to learn to grasp dexterously, so as
to be able to then grasp novel objects seen only from a single view-point.
Recently, progress has been made in data-efficient learning of generative grasp
models which transfer well to novel objects. These generative grasp models are
learned from demonstration (LfD). One weakness is that, as this paper shall
show, grasp transfer under challenging single view conditions is unreliable.
Second, the number of generative model elements rises linearly in the number of
training examples. This, in turn, limits the potential of these generative
models for generalisation and continual improvement. In this paper, it is shown
how to address these problems. Several technical contributions are made: (i) a
view-based model of a grasp; (ii) a method for combining and compressing
multiple grasp models; (iii) a new way of evaluating contacts that is used both
to generate and to score grasps. These, together, improve both grasp
performance and reduce the number of models learned for grasp transfer. These
advances, in turn, also allow the introduction of autonomous training, in which
the robot learns from self-generated grasps. Evaluation on a challenging test
set shows that, with innovations (i)-(iii) deployed, grasp transfer success
rises from 55.1% to 81.6%. By adding autonomous training this rises to 87.8%.
These differences are statistically significant. In total, across all
experiments, 539 test grasps were executed on real objects.

Robotics;Computer Vision and Pattern Recognition;Machine Learning