Robotic Grasping using Deep Reinforcement Learning
This work addresses robotic grasping for automation, presenting an incremental improvement by combining existing techniques like deep Q-learning with multi-view adaptation.
The paper tackles robotic grasping by introducing a deep reinforcement learning method with a novel Grasp-Q-Network and multi-view visual servoing, resulting in improved grasping accuracy over baseline Q-learning and single-view models in simulated and real robot experiments.
In this work, we present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. The use of a deep learning based approach reduces the complexity caused by the use of hand-designed features. Our method uses an off-policy reinforcement learning framework to learn the grasping policy. We use the double deep Q-learning framework along with a novel Grasp-Q-Network to output grasp probabilities used to learn grasps that maximize the pick success. We propose a visual servoing mechanism that uses a multi-view camera setup that observes the scene which contains the objects of interest. We performed experiments using a Baxter Gazebo simulated environment as well as on the actual robot. The results show that our proposed method outperforms the baseline Q-learning framework and increases grasping accuracy by adapting a multi-view model in comparison to a single-view model.