针对传统强化学习方法在接插装配中精确性与泛化性不足的问题，提出了一种基于优化极限学习机的机械臂接插装配技能学习与柔顺操控策略。首先，建立了基于引力搜索算法优化的极限学习机机械臂接插技能学习模型（gravitational search algorithm optimized extreme learning machine, GSA-ELM），利用机械臂示教接插操作的接触力、位置、速度等时间序列数据集优化极限学习机的初始化参数，提高了模型的精度和稳定性。其次，提出了一种基于接触力-位置预测信息的机械臂自适应力补偿柔顺控制方法，并结合位置-速度双环控制策略，实现了实时力-位反馈对接插装配的期望位置和操作速度补偿。实验结果表明，该模型能够有效保证机械臂在接插装配操作中的柔顺性和稳定性。通过UR3机械臂的通用串行总线（universal serial bus, USB）接插实验证明了该模型在实际装配中的可行性和精准性，为机械臂接插装配提供了一种新的技术路径。

In order to address the issues of insufficient accuracy and generalization in traditional reinforcement learning methods for insertion assembly tasks, a robotic arm insertion assembly skill learning and compliant control strategy based on an optimized ELM (extreme learning machine) was proposed. Firstly, GSA-ELM (gravitational search algorithm-optimized extreme learning machine) model was established for robotic arm insertion skill learning, utilizing time-series datasets of contact forces, positions, and velocities from robotic arm demonstration insertion operations to optimize the initialization parameters of the ELM, thereby enhancing accuracy and stability of the model. Secondly, an adaptive force-compensation compliant control method for robotic arms was developed based on contact force-position prediction information, integrated with a position-velocity dual-loop control strategy to achieve real-time force-position feedback compensation for desired positions and operational speeds during insertion assembly. The results show that this model effectively ensures compliance and stability in robotic arm insertion operations. Through USB (universal serial bus) insertion experiments conducted on a UR3 robotic arm, it is evident that the proposed approach demonstrates practical feasibility and precision in real-world assembly scenarios, which provides a novel technical pathway for robotic arm insertion assembly tasks.