{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T07:24:21Z","timestamp":1775892261521,"version":"3.50.1"},"reference-count":15,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2018AAA0103005"],"award-info":[{"award-number":["2018AAA0103005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>For the dual peg-in-hole compliance assembly task of upper and lower double-hole structural micro-devices, a skill-learning method is proposed. This method combines offline training in a simulation space and online training in a realistic space. In this paper, a dual peg-in-hole model is built according to the results of a force analysis, and contact-point searching methods are provided for calculating the contact force. Then, a skill-learning framework is built based on deep reinforcement learning. Both expert action and incremental action are used in training, and a reward system considers both efficiency and safety; additionally, a dynamic exploration method is provided to improve the training efficiency. In addition, based on experimental data, an online training method is used to optimize the skill-learning model continuously so that the error caused by the deviation in the offline training data from reality can be reduced. The final experiments demonstrate that the method can effectively reduce the contact force while assembling, improve the efficiency and reduce the impact of the change in position and orientation.<\/jats:p>","DOI":"10.3390\/s23208579","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:15:36Z","timestamp":1697699736000},"page":"8579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Skill-Learning Method of Dual Peg-in-Hole Compliance Assembly for Micro-Device"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7465-9444","authenticated-orcid":false,"given":"Yuting","family":"Wu","sequence":"first","affiliation":[{"name":"Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China"}]},{"given":"Juan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China"}]},{"given":"Wenrong","family":"Wu","sequence":"additional","affiliation":[{"name":"Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China"}]},{"given":"Kai","family":"Du","sequence":"additional","affiliation":[{"name":"Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/S0924-0136(02)00114-0","article-title":"Robotic grinding and polishing for turbine-vane overhaul","volume":"127","author":"Huang","year":"2002","journal-title":"J. Mater. Process. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s10846-018-0777-9","article-title":"Hybrid Compliance Control for Locomotion of Electrically Actuated Quadruped Robot","volume":"94","author":"Koco","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_3","first-page":"84","article-title":"Research on active compliance control of aircraft\u2019s washing arm based on impedance control","volume":"39","author":"Jin","year":"2022","journal-title":"J. Mach. Des."},{"key":"ref_4","unstructured":"Yin, W., Lian, D., Li, K., and Zhao, G. (2022). Manipulator force\/position hybrid control based on staged adaptation. J. Beijing Univ. Aeronaut. Astronaut., 1\u20139."},{"key":"ref_5","first-page":"379","article-title":"Industrial robot high precision peg-in-hole assembly based on hybrid force\/position control","volume":"52","author":"Wu","year":"2018","journal-title":"J. Zhejiang Univ. (Eng. Sci.)"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Anavatti, S.G., Santoso, F., and Garratt, M.A. (2015, January 15\u201317). Progress in adaptive control systems: Past, present, and future. Proceedings of the 2015 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), Surabaya, Indonesia.","DOI":"10.1109\/ICAMIMIA.2015.7537196"},{"key":"ref_7","first-page":"147","article-title":"Design of load adaptive control system for marine gear type hydraulic steering gear","volume":"45","author":"Qin","year":"2023","journal-title":"Ship Sci. Technol."},{"key":"ref_8","unstructured":"Hua, O.Y. (2014). The Design of Adaptive PID Controller Based on Neural Network. Mach. Des. Manuf., 263\u2013265."},{"key":"ref_9","first-page":"28","article-title":"Research on adaptive PID ship motion controller based on neural network algorithm","volume":"40","author":"Xin","year":"2018","journal-title":"Ship Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/3477.826961","article-title":"Neural network-based model reference adaptive control system","volume":"30","author":"Patino","year":"2000","journal-title":"IEEE Trans. Syst. Man Cybern. Part (Cybern.)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"276264","DOI":"10.1155\/2014\/276264","article-title":"Autonomous Robust Skill Generation Using Reinforcement Learning with Plant Variation","volume":"6","author":"Senda","year":"2015","journal-title":"Adv. Mech. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zou, P., Zhu, Q., Wu, J., and Xiong, R. (2020, January 25\u201329). Learning-based Optimization Algorithms Combining Force Control Strategies for Peg-in-Hole Assembly. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341678"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Beltran-Hernandez, C.C., Petit, D., Ramirez-Alpizar, I.G., and Harada, K. (2020). Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep-Reinforcement-Learning Approach. Appl. Sci., 10.","DOI":"10.3390\/app10196923"},{"key":"ref_14","first-page":"321","article-title":"Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm","volume":"45","author":"Shuang","year":"2023","journal-title":"ROBOT"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TII.2018.2868859","article-title":"Feedback Deep Deterministic Policy Gradient with Fuzzy Reward for Robotic Multiple Peg-in-Hole Assembly Tasks","volume":"15","author":"Xu","year":"2019","journal-title":"IEEE Trans. Ind. Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8579\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:45Z","timestamp":1760130585000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"references-count":15,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23208579"],"URL":"https:\/\/doi.org\/10.3390\/s23208579","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,19]]}}}