Contact-rich robotic skills remain challenging for industrial robots due to tight geometric tolerances, frictional variability, and uncertain contact dynamics, particularly when using position-controlled manipulators. This paper presents a reusable and encapsulated skill-based strategy for peg-in-hole assembly, in which adaptation is achieved through Residual Reinforcement Learning (RRL). The assembly process is represented using composite skills with explicit pre-, post-, and invariant conditions, enabling modularity, reusability, and well-defined execution semantics across task variations. Safety and sample efficiency are promoted through RRL by restricting adaptation to residual refinements within each skill during contact-rich interactions, while the overall skill structure and execution flow remain invariant. The proposed approach is evaluated in MuJoCo simulation on a UR5e robot equipped with a Robotiq gripper and trained using SAC and JAX. Results demonstrate that the proposed formulation enables robust execution of assembly skills, highlighting its suitability for industrial automation.

翻译：接触丰富的机器人技能因严格的几何公差、摩擦变异性和不确定的接触动力学，尤其是使用位置控制机械臂时，仍对工业机器人构成挑战。本文提出了一种可重用且封装化的基于技能的销孔装配策略，该策略通过残差强化学习实现自适应。装配过程使用具有显式前、后及不变条件的复合技能表示，从而在任务变化中实现模块化、可重用性及定义清晰的执行语义。通过残差强化学习，将自适应限制在每个技能在接触丰富交互中的残差微调范围内，同时保持整体技能结构和执行流程不变，从而提升安全性和样本效率。该方法在配备Robotiq夹爪的UR5e机器人上，基于MuJoCo仿真环境，使用SAC和JAX进行训练评估。结果表明，所提出的框架能够实现装配技能的鲁棒执行，凸显了其在工业自动化中的适用性。