An experience-based policy gradient method for smooth manipulation

被引：0

作者：

Wang, Yongchao ^{[1
]}

Lan, Xuguang ^{[1
]}

Feng, Chuzhen ^{[1
]}

Wan, Lipeng ^{[1
]}

Li, Jin ^{[1
]}

Liu, Yuwang ^{[1
]}

Li, Decai ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian, Shaanxi, Peoples R China

来源：

2019 9TH IEEE ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (IEEE-CYBER 2019) | 2019年

关键词：

Policy Gradient; Robot Manipulation; Deep Reinforcement Learning;

D O I：

10.1109/cyber46603.2019.9066580

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Policy gradient methods have achieved remarkable success in continuous controlling tasks. However, in robotic control, original policy gradient algorithms depend on the first succeed experience which is usually a suboptimal solution. To improve the performance, we propose an experience-based policy gradient method(EBDDPG) which guides the robot to move in a smooth way. Besides, extra OU-noise is added to the action space to improve exploration. We tested our algorithm on Gazebo simulation environment with Baxter robot. The experimental results show our method guides the robot to manipulate more smoothly and improves success rate of grasping tasks.

引用

页码：93 / 97

页数：5

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