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Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations
Zhang DP(张大朋)
2019-08-21
Conference Name2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
Conference Date2019-08-21
Conference Place温哥华
Abstract

Automatic high precision assembly of millimeter sized objects is a challenging task. Traditional methods for precision assembly require explicit programming with real robot system, which require complex parameter-tuning work. In this paper we achieve reinforcement learning of precision insertion skill based on prioritized dueling deep Q-network (DQN). The Q-function is represented by the long short term memory (LSTM) neural network, whose input is 6D force-torque feedback. According to the Q values conditioned on the current state, the skill model select 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. In addition, the reusability of the skill model in different types of insertion tasks is also investigated.

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25845
Collection精密感知与控制研究中心_精密感知与控制
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang DP. Reinforcement Learning of Robotic Precision Insertion Skill Accelerated by Demonstrations[C],2019.
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