Deep Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations
Wu, Xiapeng1,2; Zhang, Dapeng2; Qin, Fangbo2; Xu, De2
2019-08
会议名称CASE 2019: IEEE Conference on Automation Science and Engineering
会议日期2019-08-22
会议地点Vancouver, British Columbia, Canada
出版者CASE会议主办方
摘要

   Abstract—Automatic high precision assembly of millimeter sized objects is a challenging task. Traditional methods for precision assembly rely on explicit programming with real robot system, and require complex parameter-tuning work. In this paper, we realize deep reinforcement learning of precision insertion skill learning, based on prioritized dueling deep Q-network (DQN). The Q-function is represented by the long short term memory (LSTM) neural network, whose input and output are the raw 6D force-torque feedback and the Q-value,respectively. According to the Q values conditioned on the current state, the skill model selects a 6 degree-of-freedom action from the predefined action set. To accelerate the learning process, the data from demonstrations is used to pre-train the model before the DQN starts. In order to improve the insertion efficiency and safety, insertion step length is modulated based on the instant reward. Our proposed method is validated with the peg-in-hole insertion experiments on a precision assembly robot. The reusability of the skill model is also investigated with different types of insertion tasks.

语种英语
七大方向——子方向分类人工智能+制造
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/39078
专题精密感知与控制研究中心_精密感知与控制
作者单位1.中国科学院大学
2.中国科学院自动化研究所
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wu, Xiapeng,Zhang, Dapeng,Qin, Fangbo,et al. Deep Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations[C]:CASE会议主办方,2019.
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