基于深度Q网络的机器人抓取系统

Robot Grasping System Based on Deep Q-Network

针对机器人在未知环境和无模型的情况下难以对物体实现有效抓取这一问题,基于深度Q网络的方法,设计了一个机器人抓取系统。该系统可以通过与环境交互自我学习动作策略,无需先验知识和标注数据。在仿真环境和真实环境中进行抓取实验对该系统的性能进行评估,结果证明系统具有较好的完成随机场景下物体抓取任务的能力。

This paper designs a robot grasping system based on the method of Deep Q-Network to solve the problem that robot is hard to grasp effectively when environment or object is unknown.The system can learn the action strategy by interacting with the environment without prior knowledge and labeling data.The system is evaluated both in the simulation environment and real world and is proved have a good ability to complete the object grasping tasks in a variety of scenes.