Dual-Critic Deep Reinforcement Learning for Push-Grasping Synergy in Cluttered Environment

Robotic push-grasping in densely cluttered environments presents significant challenges due to unbalanced synergy and redundancy between both actions, leading to decreased grasp efficiency. In this paper, a novel double-critic deep reinforcement learning framework is introduced to optimize the push-grasping synergy for robotic manipulation in such environments, aiming to significantly reduce pre-grasping redundancy. This framework incorporates two distinct Deep Q-learning critics: Critic I selects the best course of actions based on the current state derived from visual interpretation, whereas Critic II evaluates the success rate of the current state-action pairing. To further refine the push-grasping synergy, an active double-step learning mechanism is introduced to optimize the training reward function for the pushing action, thereby enhancing its effectiveness through increased intentionality. Simulations show that the proposed framework outperforms contemporary counterparts, notably in grasping success rate and action efficiency. Finally, the framework's generalization and adaptability are demonstrated by conducting real-world experiments using novel objects without the need of retraining.