POIS: Policy-Oriented Instance Segmentation for Ambidextrous Robot Picking

摘要

Robots with a parallel-jaw gripper and suction cup is an adaptive and efficient robotic picking system. This paper proposed Policy-Oriented Instance Segmentation (POIS) for ambidextrous robots. POIS can generate a pair of target masks that allows ambidextrous robots to pick in parallel. It takes a depth image and predicts initial mask, center offset, and policy confidence map through three paralleled branches. We incorporate the initial mask with center offset to obtain candidate instances, from which we select masks of target objects for policy execution (decided with policy confidence map). We also provide a dataset that contains 6k synthetic scenes and 100 real scenes for ambidextrous picking. Trained on synthetic scenes, POIS generalizes well in real scene and is capable of handling novel objects in cluttered scenes. Our dataset and video are available at https://bit.ly/3oJj8Tu.