Learning Push-Grasp Synergy for Occluded Objects in Cluttered Environments

Abstract

Successfully executing grasping tasks within highly cluttered spaces is still a significant hurdle in robotics, especially in scenarios involving severe target occlusion. To tackle this, we present a novel self-supervised framework driven by deep reinforcement learning that enables robots to acquire push–grasp synergy for reliable manipulation under occlusions. The core contribution of this research is the target switching mechanism that dynamically selects alternative targets when the goal object is severely occluded. Furthermore, we utilize a strategy for selecting actions based on object masks to reduce the action space, thereby improving efficiency and minimizing ineffective operations. Comprehensive evaluations across both simulated and physical environments confirm that our method achieves robust grasping performance under severe or complete occlusions. Notably, the learned policy is readily transferable to physical environments and generalizes effectively to previously unseen objects.