This paper presents a new method for paralleljaw
grasping of isolated objects from depth images, under
large gripper pose uncertainty. Whilst most approaches aim
to predict the single best grasp pose from an image, our
method first predicts a score for every possible grasp pose,
which we denote the grasp function. With this, it is possible
to achieve grasping robust to the gripper’s pose uncertainty,
by smoothing the grasp function with the pose uncertainty
function. Therefore, if the single best pose is adjacent to a
region of poor grasp quality, that pose will no longer be chosen,
and instead a pose will be chosen which is surrounded by a
region of high grasp quality. To learn this function, we train
a Convolutional Neural Network which takes as input a single
depth image of an object, and outputs a score for each grasp
pose across the image. Training data for this is generated by
use of physics simulation and depth image simulation with 3D
object meshes, to enable acquisition of sufficient data without
requiring exhaustive real-world experiments. We evaluate with
both synthetic and real experiments, and show that the learned
grasp score is more robust to gripper pose uncertainty than
when this uncertainty is not accounted for.