Research Progress on Contact-rich Automated Assembly Methods：Learning Robotic Assembly Contact Force Control Skills

doi:10.3901/JME.2025.10.395

Abstract

Abstract: Due to the combined effects of manufacturing errors, positioning errors, contact deformations, and inconsistent surface qualities, the assembly contact forces exhibit random disturbances, leading to issues such as jamming, non-compliance with process requirements, and even component damage in contact-rich automated assembly. Recent research has shown that employing learning-based approaches for assembly contact control is one of the most effective strategies to address contact-rich automated assembly problems. Considering the significant progress made by reinforcement learning methods in contact-rich robotic assembly, this paper analyzes and statistically characterizes assembly features with contact-rich characteristics in the field of robotic automated assembly. It proposes discriminative indicators for identifying contact-rich assembly situations. Through an analysis of relevant literature in the field, the methods for learning contact force control in robotic automated assembly are categorized into three main types：reinforcement learning-based contact control methods, reward-engineered contact control methods, and simulation-to-reality contact control methods. Each of these categories is reviewed and analyzed. Finally, an analysis and outlook on the future development trends of learning contact-rich robotic automated assembly control skills is provided.

Key words: robot assembly, automatic assembly, contact-rich, contact force adjustment skills learning, reinforcement learning

CLC Number:

TP249
TP183

LI Mingfu, WANG Feihong, ZHU Lingfeng, LI Xiang, LEI Gaopan, LIU Yi, LI Linling, HOU Yukui, HU Yuliang. Research Progress on Contact-rich Automated Assembly Methods：Learning Robotic Assembly Contact Force Control Skills[J]. Journal of Mechanical Engineering, 2025, 61(10): 395-413.

References

[1] MARVEL J，FALCO J. Best practices and performance metrics using force control for robotic assembly[M]. New York：National Institute of Standards and Technology，2012.
[2] 刘检华，孙清超，程晖，等. 产品装配技术的研究现状，技术内涵及发展趋势[J]. 机械工程学报，2018，54(11)：2-28. LIU Jianhua，SUN Qingchao，CHENG Hui，et al. The state-of-the-art，connotation and developing trends of the products assembly technology[J]. Journal of Mechanical Engineering，2018，54(11)：2-28.
[3] LI W，WANG J，LIU M，et al. Real-time occlusion handling for augmented reality assistance assembly systems with monocular images[J]. Journal of Manufacturing Systems，2022，62：561-574.
[4] NARANG Y，STOREY K，AKINOLA I，et al. Factory：Fast contact for robotic assembly[C]// Robotics：Science and Systems 2022，June 27-July 1，2022，New York City，NY，USA：RSS，2022.
[5] 黄艳龙，徐德，谭民. 机器人运动轨迹的模仿学习综述[J]. 自动化学报，2022，48(2)：315-334. HUANG Yanlong，XU De，TAN Min. On imitation learning of robot movement trajectories：A survey[J]. Acta Automatica Sinica，2022，48(2)：315-334.
[6] 殷聪聪，张秋菊. 机器人演示学习编程技术研究综述[J]. 计算机科学与探索，2020，14(8)：1275-1287. YIN Congcong，ZHANG Qiuju. Review of research on robot programming by learning from demonstration[J]. Journal of Frontiers of Computer Science and Technology，2020，14(8)：1275-1287.
[7] JIANG J，HUANG Z，BI Z，et al. State-of-the-art control strategies for robotic pih assembly[J]. Robotics and Computer-Integrated Manufacturing，2020，65：101894.
[8] 徐德，秦方博. 机器人自动轴孔装配研究进展[J]. 智能科学与技术学报，2022，4(2)：200-211. XU De，QIN Fangbo. Research development on automated robotic peg-in-hole assembly[J]. Chinese Journal of Intelligent Science and Technology，2022，4(2)：200-211.
[9] 刘乃军，鲁涛，蔡莹皓，等. 机器人操作技能学习方法综述[J]. 自动化学报，2019，45(3)：458-470. LIU Naijun，LU Tao，CAI Yinghao，et al. A review of robot manipulation skills learning methods[J]. Acta Automatica Sinica，2019，45(3)：458-470
[10] ELGUEA-AGUINACO Í，SERRANO-MUÑOZ A，CHRYSOSTOMOU D，et al. A review on reinforcement learning for contact-rich robotic manipulation tasks[J]. Robotics and Computer-Integrated Manufacturing，2023，81：102517.
[11] HAN D，MULYANA B，STANKOVIC V，et al. A survey on deep reinforcement learning algorithms for robotic manipulation[J]. Sensors，2023，23(7)：3762.
[12] ZHAO W，QUERALTA J P，WESTERLUND T. Sim-to-real transfer in deep reinforcement learning for robotics：A survey[C]// 2020 IEEE Symposium Series on Computational Intelligence(SSCI). New York：IEEE，2020：737-744.
[13] HUA J，ZENG L，LI G，et al. Learning for a robot：Deep reinforcement learning，imitation learning，transfer learning[J]. Sensors，2021，21(4)：1278.
[14] ZHU Z，LIN K，JAIN A K，et al. Transfer learning in deep reinforcement learning：A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2023，45(11)：13344-13362.
[15] WANG H N，LIU N，ZHANG Y Y，et al. Deep reinforcement learning：A survey[J]. Frontiers of Information Technology & Electronic Engineering，2020，21(12)：1726-1744.
[16] 孔松涛，刘池池，史勇，等. 深度强化学习在智能制造中的应用展望综述[J]. 计算机工程与应用，2021，57(2)：49-59. KONG Songtao，LIU Chichi，SHI Yong，et al. Review of application prospect of deep reinforcement learning in intelligent manufacturing[J]. Computer Engineering and Applications，2021，57(2)：49-59.
[17] 陈佳盼，郑敏华. 基于深度强化学习的机器人操作行为研究综述[J]. 机器人，2022，44(2)：236-256. CHEN Jiapan，ZHENG Minhua. A survey of robot manipulation behavior research based on deep reinforcement learning[J]. Robot，2022，44(2)：236-256.
[18] LI C，ZHENG P，YIN Y，et al. Deep reinforcement learning in smart manufacturing：A review and prospects[J]. CIRP Journal of Manufacturing Science and Technology，2023，40：75-101.
[19] ABU-DAKKA F J，NEMEC B，KRAMBERGER A，et al. Solving peg-in-hole tasks by human demonstration and exception strategies[J]. Industrial Robot：An International Journal，2014，41(6)：575-584.
[20] ZHU Z，HU H. Robot learning from demonstration in robotic assembly：A survey[J]. Robotics，2018，7(2)：17.
[21] PARK H，PARK J，LEE D H，et al. Compliant peg-in-hole assembly using partial spiral force trajectory with tilted peg posture[J]. IEEE Robotics and Automation Letters，2020，5(3)：4447-4454.
[22] JHA D K，ROMERES D，JAIN S，et al. Design of adaptive compliance controllers for safe robotic assembly[C]// 2023 European Control Conference(ECC). IEEE，2023：1-8. https://doi.org/10.23919/ECC57647.2023.10178229.
[23] LIU G，YAO B，XU W，et al. Optimizing non-diagonal stiffness matrix of compliance control for robotic assembly using deep reinforcement learning[C]//Journal of Physics：Conference Series. IOP Publishing，2022，2402(1)：012013. https://dx.doi.org/10.1088/1742-6596/2402/1/012013.
[24] LEE M A，ZHU Y，ZACHARES P，et al. Making sense of vision and touch：Learning multimodal representations for contact-rich tasks[J]. IEEE Transactions on Robotics，2020，36(3)：582-596.
[25] MICHALOS G，MAKRIS S，CHRYSSOLOURIS G. The new assembly system paradigm[J]. International Journal of Computer Integrated Manufacturing，2015，28(12)：1252-1261.
[26] MAKRIS S，TSAROUCHI P，SURDILOVIC D，et al. Intuitive dual arm robot programming for assembly operations[J]. CIRP Annals，2014，63(1)：13-16.
[27] SAGARDIA M，HULIN T. Multimodal evaluation of the differences between real and virtual assemblies[J]. IEEE Transactions on Haptics，2017，11(1)：107-118.
[28] CHANG R J，JAU J C. Augmented reality in peg-in-hole microassembly operations[J]. International Journal of Automation Technology，2016，10(3)：438-446.
[29] LEE D Y，CHUNG S Y，HWANG M J，et al. Adaptive modeling of robotic assembly using augmented Petri nets[C]//2007 American Control Conference. New York：IEEE，2007：3600-3605. https://doi.org/10.1109/ACC.2007.4282773
[30] BODENHAGEN L，FUGL A R，WILLATZEN M，et al. Learning peg-in-hole actions with flexible objects[C]// In 4th International Conference on Agents and Artificial Intelligence(ICAART)，Vilamoura，2012：624-631.
[31] BODENHAGEN L，FUGL A R，JORDT A，et al. An adaptable robot vision system performing manipulation actions with flexible objects[J]. IEEE Transactions on Automation Science and Engineering，2014，11(3)：749-765.
[32] YANCHUN X，YUEWEI B，YAFEI H. Assembly strategy study on the elastic deformable peg in hole[C]// 2010 The 2nd International Conference on Industrial Mechatronics and Automation：Volume 1. New York：IEEE，2010：193-197.
[33] JASIM I F，PLAPPER P W. Contact-state monitoring of force-guided robotic assembly tasks using expectation maximization-based gaussian mixtures models[J]. The International Journal of Advanced Manufacturing Technology，2014，73：623-633.
[34] JASIM I F，PLAPPER P W. Contact-state modeling of robotic assembly tasks using gaussian mixture models[J]. Procedia Cirp，2014，23：229-234.
[35] TAKAHASHI J，FUKUKAWA T，FUKUDA T. Passive alignment principle for robotic assembly between a ring and a shaft with extremely narrow clearance[J]. IEEE/ ASME Transactions on Mechatronics，2015，21(1)：196- 204.
[36] WATSON J，MILLER A，CORRELL N. Autonomous industrial assembly using force，torque，and rgb-d sensing[J]. Advanced Robotics，2020，34(7-8)：546-559.
[37] SLOTH C，KRAMBERGER A，ITURRATE I. Towards easy setup of robotic assembly tasks[J]. Advanced Robotics，2020，34(7-8)：499-513.
[38] 潘柏松，颜天野，胡鑫达，等. 基于几何约束与隐马尔可夫链模型的轴孔装配策略[J]. 计算机集成制造系统，2022，28(12)：3768-3778. PAN Baisong，YAN Tianye，HU Xinda，et al. Peg-in-hole assembly strategy based on geometric constraint and hidden Markov model[J]. Computer Integrated Manufacturing Systems，2022，28(12)：3768-3778.
[39] 罗威，李明富，赵文权，等. 基于力-位图像学习的工业机器人柔顺装配方法研究[J]. 机械工程学报，2022，58(21)：69-77. LUO Wei，LI Mingfu，ZHAO Wenquan，et al. Research on flexible assembly method of industrial robot based on force-pose-image learning[J]. Journal of Mechanical Engineering，2022，58(21)：69-77.
[40] LEVINE S，ABBEEL P. Learning neural network policies with guided policy search under unknown dynamics[J]. Advances in Neural Information Processing Systems，2014，27：1071-1079.
[41] WIRNSHOFER F，SCHMITT P S，MEISTER P，et al. State estimation in contact-rich manipulation[C]//2019 International Conference on Robotics and Automation (ICRA). New York：IEEE，2019：3790-3796.
[42] KHADER P，YIN H，KRAGIC D. Probabilistic model learning and long-term prediction forcontact-rich manipulation tasks[J]. CoRR，ArXiv：1909.04915vl，2019.
[43] 刘乃龙，刘钊铭，崔龙. 基于深度强化学习的仿真机器人轴孔装配研究[J]. 计算机仿真，2019，36(12)：296-301. LIU Nailong，LIU Zhaoming，CUI Long. Deep reinforcement learning based robotic assembly in simulation[J]. Computer Simulation，2019，36(12)：296-301.
[44] INOUE T，DE MAGISTRIS G，MUNAWAR A，et al. Deep reinforcement learning for high precision assembly tasks[C]// 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). New York：IEEE，2017：819-825.
[45] ZOU P，ZHU Q，WU J，et al. Learning-based optimization algorithms combining force control strategies for peg-in-hole assembly[C]// 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). New York：IEEE，2020：7403-7410.
[46] ROUILLARD T. A deep-reinforcement-learning approach to the peg-in-hole task with goal uncertainties[D]. Perth：Curtin University，2020.
[47] OIKAWA M，KUSAKABE T，KUTSUZAWA K，et al. Reinforcement learning for robotic assembly using non-diagonal stiffness matrix[J]. IEEE Robotics and Automation Letters，2021，6(2)：2737-2744.
[48] YIN R，WU H，LI M，et al. Rgb-d-based robotic grasping in fusion application environments[J]. Applied Sciences，2022，12(15)：7573.
[49] LEE Y S，VUONG N，ADRIAN N，et al. Integrating force-based manipulation primitives with deep learning- based visual servoing for robotic assembly[C/CD]//ICRA 2022 Workshop：Reinforcement Learning for Contact- Rich Manipulation. May 27，2022，Philadelphia(PA)，USA：ICRA，2022.
[50] VUONG N，PHAM H，PHAM Q C. Learning sequences of manipulation primitives for robotic assembly[C]//2021 IEEE International Conference on Robotics and Automation (ICRA). New York：IEEE，2021：4086-4092.
[51] LIU Y，VERLEYSEN A，WYFFELS F. A robust and safe strategy for robotic assembly[C/OL]//Embracing Contacts- Workshop at ICRA 2023. June 2，2023，London：ICRA，2023.
[52] REN T，DONG Y，WU D，et al. Learning-based variable compliance control for robotic assembly[J]. Journal of Mechanisms and Robotics，2018，10(6)：061008.
[53] XU J，HOU Z，WANG W，et al. Feedback deep deterministic policy gradient with fuzzy reward for robotic multiple peg-in-hole assembly tasks[J]. IEEE Transactions on Industrial Informatics，2018，15(3)：1658-1667.
[54] FAN Y，LUO J，TOMIZUKA M. A learning framework for high precision industrial assembly[C]//2019 International Conference on Robotics and Automation(ICRA). New York：IEEE，2019：811-817.
[55] LI F，JIANG Q，QUAN W，et al. Manipulation skill acquisition for robotic assembly based on multi-modal information description[J]. IEEE Access，2019，8：6282- 6294.
[56] BELTRAN-HERNANDEZ C C，PETIT D，RAMIREZ- ALPIZAR I G，et al. Learning force control for contact- rich manipulation tasks with rigid position-controlled robots[J]. IEEE Robotics and Automation Letters，2020，5(4)：5709-5716.
[57] HOPPE S，GIFTTHALER M，KRUG R，et al. Sample- efficient learning for industrial assembly using Qgraph- bounded DDPG[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). New York：IEEE，2020：9080-9087.
[58] ZHAO X，ZHAO H，CHEN P，et al. Model accelerated reinforcement learning for high precision robotic assembly[J]. International Journal of Intelligent Robotics and Applications，2020，4(2)：202-216.
[59] LUO J，SUSHKOV O，PEVCEVICIUTE R，et al. Robust multi-modal policies for industrial assembly via reinforcement learning and demonstrations：A large-scale study[C/CD]//Robotics：Science and Systems 2021，July 12-16，2021，Held Virtually.
[60] APOLINARSKA A A，PACHER M，LI H，et al. Robotic assembly of timber joints using reinforcement learning[J]. Automation in Construction，2021，125：103569.
[61] LUO J，LI H. A learning approach to robot-agnostic force-guided high precision assembly[C]//2021 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). New York：IEEE，2021：2151-2157.
[62] LI Y，XU D. Skill learning for robotic insertion based on one-shot demonstration and reinforcement learning[J]. International Journal of Automation and Computing，2021，18(3)：457-467.
[63] LI J，PANG D，ZHENG Y，et al. A flexible manufacturing assembly system with deep reinforcement learning[J]. Control Engineering Practice，2022，118：104957.
[64] GAI Y，ZHANG J，WU D，et al. Local connection reinforcement learning method for efficient robotic peg-in-hole assembly[J]. Engineering Applications of Artificial Intelligence，2024，133：108520.
[65] 双丰，卢万玉，李少东，等. 基于强化学习的机器人轴孔装配算法[J]. 机器人，2023，45(3)：321-332. SHUANG Feng，LU Wanyu，LI Shaodong，et al. Robotic peg-in-hole assembly algorithm based on reinforcement learning[J]. Robot，2023，45(3)：321-332.
[66] 王竣禾，姜勇. 基于深度强化学习的动态装配算法[J]. 智能系统学报，2023，18(1)：2-11. WANG Junhe，JIANG Yong. Dynamic assembly algorithm based on deep reinforcement learning[J]. CAAI Transactions on Intelligent Systems，2023，18(1)：2-11.
[67] WU W，ZHOU H，GUO Y，et al. Peg-in-hole assembly in live-line maintenance based on generative mapping and searching network[J]. Robotics and Autonomous Systems，2021，143：103797.
[68] ZANG Y，WANG P，ZHA F，et al. Geometric-feature representation based pre-training method for reinforcement learning of peg-in-hole tasks[J]. New York：IEEE Robotics and Automation Letters，2023，8(6)：3478-3485.
[69] DANG R，HOU Z，YANG W，et al. Fusing vision and force：A framework of reinforcement learning for elastic peg-in-hole assembly[C]//2023 WRC Symposium on Advanced Robotics and Automation(WRC SARA). New York：IEEE，2023：1-6.
[70] WANG G，YI J，JI X. Research on intelligent peg-in-hole assembly strategy based on deep reinforcement learning[C]// 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science(ACCTCS). New York：IEEE，2023：468-473.
[71] BÁLINT B A，LÄMMLE A，SALTERIS Y，et al. Benchmark of the physics engine mujoco and learning- based parameter optimization for contact-rich assembly tasks[J]. Procedia CIRP，2023，119：1059-1064.
[72] THOMAS G，CHIEN M，TAMAR A，et al. Learning robotic assembly from cad[C]//2018 IEEE International Conference on Robotics and Automation(ICRA). New York：IEEE，2018：3524-3531.
[73] LUO J，SOLOWJOW E，WEN C，et al. Deep reinforcement learning for robotic assembly of mixed deformable and rigid objects[C]// 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). New York：IEEE，2018：2062-2069.
[74] LUO J，SOLOWJOW E，WEN C，et al. Reinforcement learning on variable impedance controller for high-precision robotic assembly[C]//2019 International Conference on Robotics and Automation(ICRA). New York：IEEE，2019：3080- 3087.
[75] LUO J，ABBEEL P. Reinforcement learning for robotic assembly with force control[D]. California：Berkeley，2020.
[76] HOPPE S，LOU Z，HENNES D，et al. Planning approximate exploration trajectories for model-free reinforcement learning in contact-rich manipulation[J]. IEEE Robotics and Automation Letters，2019，4(4)：4042-4047.
[77] WANG Y，BELTRAN-HERNANDEZ C C，WAN W，et al. Hybrid trajectory and force learning of complex assembly tasks：A combined learning framework[J]. IEEE Access，2021，9：60175-60186.
[78] WANG Y，BELTRAN-HERNANDEZ C C，WAN W，et al. Robotic imitation of human assembly skills using hybrid trajectory and force learning[C]//2021 IEEE International Conference on Robotics and Automation (ICRA). New York：IEEE，2021：11278-11284.
[79] LIU Y，ROMERES D，JHA D K，et al. Understanding multi-modal perception using behavioral cloning for peg-in-a-hole insertion tasks[C]//RSS 2020 workshop on Advances & Challenges in Imitation Learning for Robotics，July 22，2020，Broadway，Cambridge，Massac- husetts，2020：TR2020-110.
[80] LIU Q，JI Z，XU W，et al. Knowledge-guided robot learning on compliance control for robotic assembly task with predictive model[J]. Expert Systems with Applications，2023，234：121037.
[81] MING J，BARGMANN D，CAO H，et al. Flexible gear assembly with visual servoing and force feedback[C]// 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS)，2023：8276-8282.
[82] SIMONIČ M，UDE A，NEMEC B. Hierarchical learning of robotic contact policies[J]. Robotics and Computer- Integrated Manufacturing，2024，86：102657.
[83] TANAKA K，YONETANI R，HAMAYA M，et al. Trans-am：Transfer learning by aggregating dynamics models for soft robotic assembly[C]//2021 IEEE International Conference on Robotics and Automation(ICRA). New York：IEEE，2021：4627-4633.
[84] YUAN C，SHI Y，FENG Q，et al. Sim-to-real transfer of robotic assembly with visual inputs using cyclegan and force control[C]// 2022 IEEE International Conference on Robotics and Biomimetics(ROBIO). New York：IEEE，2022：1426-1432.
[85] SHI Y，YUAN C，TSITOS A，et al. A sim-to-real learning-based framework for contact-rich assembly by utilizing cyclegan and force control[J]. IEEE Transactions on Cognitive and Developmental Systems，2023，15(4)：2144-2155.
[86] SHI Y. Visual and force-driven-based assembly learning using collaborative robots[D]. Hamburg：Universitaet Hamburg，2023.
[87] GHASEMIPOUR S K S，KATAOKA S，DAVID B，et al. Blocks assemble! learning to assemble with large-scale structured reinforcement learning[C]//International Conference on Machine Learning，July 17-23，2022 Baltimore，Maryland，USA，PMLR 162，2022：7435-7469.
[88] JIN P，LIN Y，SONG Y，et al. Vision-force-fused curriculum learning for robotic contact-rich assembly tasks[J]. Frontiers in Neurorobotics，2023，17：1280773.
[89] AHN K H，NA M，SONG J B. Robotic assembly strategy via reinforcement learning based on force and visual information[J]. Robotics and Autonomous Systems，2023，164：104399.
[90] BELTRAN-HERNANDEZ C C，PETIT D，RAMIREZ- ALPIZAR I G，et al. Variable compliance control for robotic peg-in-hole assembly：A deep-reinforcement- learning approach[J]. Applied Sciences，2020，10(19)：6923.
[91] KASPAR M，OSORIO J D M，BOCK J. Sim2real transfer for reinforcement learning without dynamics randomization[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). New York：IEEE，2020：4383-4388.
[92] HOU Z，FEI J，DENG Y，et al. Data-efficient hierarchical reinforcement learning for robotic assembly control applications[J]. IEEE Transactions on Industrial Electronics，2020，68(11)：11565-11575.
[93] WANG Y，BELTRAN-HERNANDEZ C C，WAN W，et al. An adaptive imitation learning framework for robotic complex contact-rich insertion tasks[J]. Frontiers in Robotics and AI，2022，8：777363.
[94] BELTRAN H C C. Reinforcement learning for contact-rich assembly[D]. Osaka：Osaka University，2022.
[95] TANG B，LIN M A，AKINOLA I，et al. Industreal：Transferring contact-rich assembly tasks from simulation to reality[C]//Robotics：Science and Systems 2023，July 10-14，2023，Daegu，Republic of Korea.
[96] WHITNEY D E. Quasi-static assembly of compliantly supported rigid parts[J]. Journal of Dynamic Systems，Measurement，and Control，1982，104(1)：65-77.
[97] HOGAN N. Impedance control：An approach to manipulation[C]//1984 American Control Conference. June 6-8，1984，SanDiego，CA，USA. New York：IEEE，1984：304-313.
[98] 李侃，汪法根，廖正菊，等. 一种用于多轴孔装配的机器人系统研究[J]. 机械设计与研究，2003，19(3)：25-26. LI Kan，WANG Fagen，LIAO Zhengju，et al. Study on a robotic system for multiaxial bore assembling[J]. Machine Design and Research，2003，19(3)：25-26.
[99] STOLT A，LINDEROTH M，ROBERTSSON A，et al. Force controlled assembly of emergency stop button[C]// 2011 IEEE International Conference on Robotics and Automation. New York：IEEE，2011：3751-3756.
[100] PARK H，BAE J H，PARK J H，et al. Intuitive peg-in-hole assembly strategy with a compliant manipulator[C/CD]//IEEE ISR 2013. New York：IEEE，2013.
[101] 曾冰，李明富，张翼，等. 基于萤火虫算法的装配序列规划研究[J]. 机械工程学报，2013，49(11)：177-184. ZENG Bing，LI Mingfu，ZHANG Yi，et al. Research on assembly sequence planning based on firefly algorithm[J]. Journal of Mechanical Engineering，2013，49(11)：177-184.
[102] 魏明明，傅卫平，蒋家婷，等. 操作机器人轴孔装配的行为动力学控制策略[J]. 机械工程学报，2015，51(5)：14-21. WEI Mingming，FU Weiping，JIANG Jiating，et al. Dynamics of behavior control strategy in peg-in-hole assembly task of manipulator[J]. Journal of Mechanical Engineering，2015，51(5)：14-21.
[103] TANG T，LIN H C，ZHAO Y，et al. Autonomous alignment of peg and hole by force/torque measurement for robotic assembly[C]// 2016 IEEE International Conference on Automation Science and Engineering (CASE). New York：IEEE，2016：162-167.
[104] 吴炳龙，曲道奎，徐方. 基于力/位混合控制的工业机器人精密轴孔装配[J]. 浙江大学学报，2018，52(2)：379-386. WU Binglong，QU Daokui，XU Fang. Industrial robot high precision peg-in-hole assembly based on hybrid force/position control[J]. Journal of Zhejiang University，2018，52(2)：379-386.
[105] 徐远，宋爱国，李会军. 基于机器视觉和力反馈的自动装配技术研究[J]. 测控技术，2019，38(4)：11-16. XU Yuan，SONG Aiguo，LI Huijun. Research on automatic assembly technology based on machine vision and force feedback[J]. Measurement and Control Technology，2019，38(4)：11-16.
[106] 董悫，张立建，易旺民，等. 基于动力学前馈的空间机器人多销孔装配力柔顺控制[J]. 机械工程学报，2020，55(4)：207-217. DONG Que，ZHANG Lijian，YI Wangmin，et al. Force compliance control of multi-peg-in-hole assembling by space robot based on dynamic feedforward[J]. Journal of Mechanical Engineering，2020，55(4)：207-217.
[107] 陈婵娟，赵飞飞，李承，等. 多传感器协助机器人精确装配[J]. 机械设计与制造，2020(3)：281-284. CHEN Chanjuan，ZHAO Feifei，LI Cheng，et al. Multi-sensor assisted robotic accurate assembly[J]. Machinery Design and Manufacture，2020(3)：281-284.
[108] 胡瑞钦，张立建，孟少华，等. 基于柔顺控制的航天器大部件机器人装配技术[J]. 机械工程学报，2018， 54(11)：85-93. HU Ruiqin，ZHANG Lijian，MENG Shaohua，et al. Robot assembly technology based on compliance control[J]. Journal of Mechanical Engineering，2018，54(11)：85-93.
[109] BROWN T，MANN B，RYDER N，et al. Language models are few-shot learners[C]// Advances in Neural Information Processing Systems 33(NeurIPS 2020). USA：Curran Associates，Inc.，2020：1877-1901.
[110] AL-SHEDIVAT M，BANSAL T，BURDA Y，et al. Continuous adaptation via meta-learning in nonstationary and competitive environments[C]//International Conference on Learning Representations(ICLR)，April 30-May 3，2018，Vancouver Convention Center，Vanc-ouver，BC，Canada：ICLR，2018.
[111] SILVER D，SINGH S，PRECUP D，et al. Reward is enough[J]. Artificial Intelligence，2021，299：103535.
[112] SCHRITTWIESER J，ANTONOGLOU I，HUBERT T，et al. Mastering atari，go，chess and shogi by planning with a learned model[J]. Nature，2020，588(7839)：604-609.
[113] LEVINE S，PASTOR P，KRIZHEVSKY A，et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J]. The International Journal of Robotics Research，2018，37(4-5)：421-436.
[114] LEVINE S，FINN C，DARRELL T，et al. End-to-end training of deep visuomotor policies[J]. Journal of Machine Learning Research，2016，17(39)：1-40.
[115] BARGMANN D，TENBROCK P，HALT L，et al. Unobstructed programming-by-demonstration for force- based assembly utilizing external force-torque sensors[C]// 2021 IEEE International Conference on Systems，Man，and Cybernetics(SMC). New York：IEEE，2021：119-124.
[116] ARGÜZ S H. Learning of tasks with robot programming by demonstration[D]. Turkey：Izmir Institute of Technology，2022.
[117] LIU N，ZHOU X，LIU Z，et al. Learning peg-in-hole assembly using cartesian dmps with feedback mechanism[J]. Assembly Automation，2020，40(6)：895-904.
[118] 张浩，陈成军，潘勇. 基于视觉的工业机器人装配演示示教研究.[J]. 机床与液压，2023，51(19)：25-31. ZHANC Hao，CHEN Chengjun，PAN Yong. Research on demonstration and teaching of industrial robot assembly based on vision[J]. Machine Tool and Hydraulics，2023，51(19)：25-31.
[119] LEVINE S，KOLTUN V. Guided policy search[C]// International Conference on Machine Learning. June 17-19，2013，Tlanta，Georgia，USA：PMLR 28，2013：1-9.
[120] SCHULMAN J，LEVINE S，ABBEEL P，et al. Trust region policy optimization[C]// International Conference on Machine Learning，July 7-9，2015，Lille，France：PMLR 37，2015：1889-1897.
[121] DEWEY D. Reinforcement learning and the reward engineering principle[C]// 2014 AAAI Spring Symposium Series. March 24-26，2014，Stanford University，Palo Alto，California，USA：AAAI，2014.
[122] SUTTON R S，BARTO A G. Reinforcement learning：An introduction[M]. Massachusetts：MIT Press，2018.
[123] ACHIAM J，HELD D，TAMAR A，et al. Constrained policy optimization[C]//International Conference on Machine Learning. August 6-11，2017，International Convention Centre，Sydney，Australia：PMLR 70，2017：22-31.
[124] GAI Y，WANG B，ZHANG J，et al. Robotic assembly control reconfiguration based on transfer reinforcement learning for objects with different geometric features[J]. Engineering Applications of Artificial Intelligence，2024，129：107576.
[125] MATIISEN T，OLIVER A，COHEN T，et al. Teacher-student curriculum learning[J]. IEEE Transactions on Neural Networks and Learning Systems，2020，31(9)：3732-3740.
[126] RAISI M，NOOHIAN A H，KHODAYGAN S. Autonomous robotic assembly process based on hierarchical reinforcement learning[C]//The 30th Annual International Conference of lranian Society of Mechanical Engineers. May 10-12，2022，Tehran，Iran：ISME2022-IC1484.
[127] NARVEKAR S，PENG B，LEONETTI M，et al. Curriculum learning for reinforcement learning domains：A framework and survey[J]. The Journal of Machine Learning Research，2020，21(1)：7382-7431.
[128] WU Z，LIAN W，UNHELKAR V，et al. Learning dense rewards for contact-rich manipulation tasks[C/OL]//2021 IEEE International Conference on Robotics and Automation(ICRA). New York：IEEE，2021：6214-6221.