The　positional　information　of　objects　is　crucial　to　enable　robots　to　perform　grasping　and　pushing　manipulations　in　clutter.　To　effectively　perform　grasping　and　pushing　manipulations,　robots　need　to　perceive　the　position　information　of　objects,　including　the　coordinates　and　spatial　relationship　between　objects　(e.g.,　proximity,　adjacency).　The　authors　propose　an　end-to-end　position-aware　deep　Q-learning　framework　to　achieve　efficient　collaborative　pushing　and　grasping　in　clutter.　Specifically,　a　pair　of　conjugate　pushing　and　grasping　attention　modules　are　proposed　to　capture　the　position　information　of　objects　and　generate　high-quality　affordance　maps　of　operating　positions　with　features　of　pushing　and　grasping　operations.　In　addition,　the　authors　propose　an　object　isolation　metric　and　clutter　metric　based　on　instance　segmentation　to　measure　the　spatial　relationships　between　objects　in　cluttered　environments.　To　further　enhance　the　perception　capacity　of　position　information　of　the　objects,　the　authors　associate　the　change　in　the　object　isolation　metric　and　clutter　metric　in　cluttered　environment　before　and　after　performing　the　action　with　reward　function.　A　series　of　experiments　are　carried　out　in　simulation　and　real-world　which　indicate　that　the　method　improves　sample　efficiency,　task　completion　rate,　grasping　success　rate　and　action　efficiency　compared　to　state-of-the-art　end-to-end　methods.　Noted　that　the　authors＇　system　can　be　robustly　applied　to　real-world　use　and　extended　to　novel　objects.　Supplementary　material　is　available　at　.