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) tri-mode grasping learning 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：在杂乱场景中的物体拣选是机器人操作领域广泛研究的课题，然而双手机器人拣选仍是一个重要且具有挑战性的问题。我们发现不同抓取动作（抓取与吸取）的融合能扩大机器人可拣选物体的范围，而抓取动作与非抓取动作（推动）的融合能扩展双手机器人的拣选空间。本文提出一种基于推、抓、吸三模式融合的学习网络，通过整合不同抓取动作及抓取与非抓取动作的融合，实现双手机器人拣选。PGS的抓取分支以点云为输入，通过多任务点云学习生成杂乱场景中6自由度抓取与吸取的拣选配置；非抓取分支以深度图像为输入，生成实例分割图与推动配置，协同抓取动作完成超出单臂空间的物体拣选。PGS在真实场景中具有良好的泛化能力，并实现了当前最优的拣选性能。