  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.
