Integration of Deep Q-Learning with a Grasp Quality Network for Robot Grasping in Cluttered Environments

During the movement of a robotic arm, collisions can easily occur if the arm directly grasps at multiple tightly stacked objects, thereby leading to grasp failures or machine damage. Grasp success can be improved through the rearrangement or movement of objects to clear space for grasping. This paper presents a high-performance deep Q-learning framework that can help robotic arms to learn synchronized push and grasp tasks. In this framework, a grasp quality network is used for precisely identifying stable grasp positions on objects to expedite model convergence and solve the problem of sparse rewards caused during training because of grasp failures. Furthermore, a novel reward function is proposed for effectively evaluating whether a pushing action is effective. The proposed framework achieved grasp success rates of 92% and 89% in simulations and real-world experiments, respectively. Furthermore, only 200 training steps were required to achieve a grasp success rate of 80%, which indicates the suitability of the proposed framework for rapid deployment in industrial settings.