论文标题
机器人抓握使用深度加固学习
Robotic Grasping using Deep Reinforcement Learning
论文作者
论文摘要
在这项工作中，我们提出了一种基于强化学习的方法，以解决使用Visio-Motor反馈来解决机器人抓握的问题。基于深度学习的方法的使用降低了使用手工设计的功能引起的复杂性。我们的方法使用非政策增强学习框架来学习掌握策略。我们使用双重Q学习框架以及一种新颖的Grasp-Q-Network来输出用于学习最大化选择成功的grasps的概率。我们提出了一种视觉宣传机制，该机制使用了多视图摄像头设置，该设置观察了包含感兴趣对象的场景。我们使用百特凉亭模拟环境以及实际机器人进行了实验。结果表明，我们提出的方法比基线Q学习框架优于基线Q学习框架，并通过与单视图模型相比，通过调整多视图模型来提高抓地精度。
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.
