Robotic Grasping in Simulation Using Deep Reinforcement Learning

Abstract

In robotics, manipulators are recently becoming one of the prominent fields of interest for different types of applications. One of the usual functionalities performed by manipulators is grasping. Grasping means simply holding an object. In order to perform a grasping task, each manipulator needs a gripper mounted at the end effector of them. In this paper, a method based on deep reinforcement learning is presented to deal with the issue of robotic grasping employing only vision feedback. The combination of deep learning with dueling architecture, a variant of Q-learning, brings the complexity caused by the use of handcrafted features to a humbler state. Our method employs the Dueling Deep Q-learning Network(DDQN) to learn the grasping policy. Our proposed system employs a visual structure that uses a Kinect camera setup that spots the scene that possesses the object of interest. We realized our experiments by utilizing Webots simulator environment. The results show that our proposed dueling architecture enables our Reinforcement Learning(RL) agent to perform well enough to fulfill the grasping task.