Object picking in cluttered scenes is a widely investigated field of robot manipulation, however, ambidextrous robot picking is still an important and challenging issue. We found the fusion of different prehensile actions (grasp and suction) can expand the range of objects that can be picked by robot, and the fusion of prehensile action and nonprehensile action (push) can expand the picking space of ambidextrous robot. In this paper, we propose a "Push-Grasp-Suction" (PGS) integrated network for ambidextrous robot picking through the fusion of different prehensile actions and the fusion of prehensile action and nonprehensile aciton. The prehensile branch of PGS takes point clouds as input, and the 6-DoF picking configuration of grasp and suction in cluttered scenes are generated by multi-task point cloud learning. The nonprehensile branch with depth image input generates instance segmentation map and push configuration, cooperating with the prehensile actions to complete the picking of objects out of single-arm space. PGS generalizes well in real scene and achieves state-of-the-art picking performance.

翻译：杂乱场景中的物体抓取是机器人操作领域广泛研究的课题，然而双臂机器人抓取仍是一项重要且具有挑战性的问题。我们发现，不同抓取动作（抓取和吸取）的融合能够扩展机器人可抓取物体的范围，而抓取动作与非抓取动作（推）的融合则能扩展双臂机器人的操作空间。本文提出了一种"推-抓-吸"（PGS）融合网络，通过不同抓取动作的融合以及抓取动作与非抓取动作的融合实现双臂机器人抓取。PGS的抓取分支以点云作为输入，通过多任务点云学习生成杂乱场景中抓取和吸取的六自由度抓取配置。非抓取分支以深度图像作为输入，生成实例分割图和推动作配置，与抓取动作协同完成超出单臂操作空间物体的抓取。PGS在真实场景中具有良好的泛化性能，并实现了目前最先进的抓取效果。