Object Singulation by Nonlinear Pushing for Robotic Grasping

In this study, we aim at grasping a single target object in a cluttered environment using a robotic arm. While dexterous grasp for various shapes of objects is not considered in this work, we focus on developing the method to mitigate clutter near the target object as soon as quickly. For this purpose, we propose a method to generate nonlinear pushing motions for object singulation based on an off-the-shelf machine learning algorithm and a typical semantic segmentation algorithm. Through experiments, we show that the success rate of robotic grasping is considerably improved by the proposed pushing behavior. And notably, the nonlinear pushing trajectories allows the robot to perform singulation of the target object in a cluttered environment with fewer trials than linear pushing usually pursued in related works.