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Special Topics in Reinforcement Learning
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 4
1018-1024

Robotic pin-hole assembly method integrating prior knowledge and guided policy search

Chen Haojie1,2,3,4
Dong Qingwei1,2,3,4
Liu Ruikai1,2,3,4
Zeng Peng1,2,3
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
3. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2024.08.0324

Abstract

In modern industrial automation, robots play a crucial role in performing complex assembly tasks. Reinforcement learning provides an effective approach for robot strategy learning, but it encounters challenges such as low sampling efficiency and poor sample quality during the early stages of strategy training. These challenges slow down algorithm convergence and increase the risk of getting stuck in local optima. To address these issues, this paper presented a robot trajectory planning method that integrated prior knowledge with the guided policy search algorithm. The method drew on prior knowledge from human expert demonstrations and historical task data to build an initial policy and stored this knowledge in an experience pool to improve learning efficiency. The guided policy search algorithm optimized the policy online, gradually enhancing the precision and adaptability of the strategy. The research team conducted experiments on a robotic pin-hole assembly task and found that this method significantly improved strategy learning efficiency, reduced training time, and minimized trial-and-error iterations. The results show that integrating prior knowledge effectively improves the learning efficiency of reinforcement lear-ning, allowing robots to quickly obtain strategies that can complete assembly tasks.

Foundation Support

国家自然科学基金资助项目(92267301,92067205,92267205)
辽宁省自然科学基金资助项目(2024-MSBA-83)
机器人学国家重点实验室(2023-Z15)
国家博士后基金资助项目(GZB20230805)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0324
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 4
Section: Special Topics in Reinforcement Learning
Pages: 1018-1024
Serial Number: 1001-3695(2025)04-007-1018-07

Publish History

[2024-12-25] Accepted Paper
[2025-04-05] Printed Article

Cite This Article

陈豪杰, 董青卫, 刘锐楷, 等. 融合先验知识与引导策略搜索的机器人轴孔装配方法 [J]. 计算机应用研究, 2025, 42 (4): 1018-1024. (Chen Haojie, Dong Qingwei, Liu Ruikai, et al. Robotic pin-hole assembly method integrating prior knowledge and guided policy search [J]. Application Research of Computers, 2025, 42 (4): 1018-1024. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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