Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty
This addresses grasping reliability for robotics in uncertain environments, but is incremental as it builds on existing grasp prediction methods.
The paper tackles the problem of parallel-jaw grasping under large gripper pose uncertainty by predicting a grasp function that scores all possible poses, enabling robust grasping through smoothing with uncertainty. Results show improved robustness in synthetic and real experiments compared to methods ignoring uncertainty.
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.