This paper presents a new method for parallel-jaw 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.

本文提出了一种新的方法，使用深度图像对孤立对象进行平行夹持，解决大型夹持器姿态不确定性的问题，该方法训练卷积神经网络进行评分以确定触抓姿态，最终实验表明该系统比未考虑不确定性时更具鲁棒性。