用强化学习让机械臂自适应完成插孔装配，更安全高效。
Learning-Based Strategy for Composite Robot Assembly Skill Adaptation
- 通过残差强化学习，在保持整体流程不变的前提下微调接触动作。
- 在仿真中实现稳定装配，对摩擦和公差变化有强鲁棒性。
- 适合工业自动化中需要高精度、重复性装配的场景。
由于几何公差严格、摩擦变化大以及接触动力学不确定，基于位置控制的机械臂在执行高接触密度任务时仍具挑战性。本文提出一种可复用、模块化的复合技能策略，用于圆柱销插入孔洞的装配任务，通过残差强化学习（RRL）实现自适应。装配过程以包含显式前置、后置和不变条件的复合技能表示，支持模块化、可复用与清晰的执行语义。通过将适应限制在每个技能内的残差修正，RRL提升了安全性与样本效率，同时保持整体技能结构和执行流程不变。该方法在配备Robotiq夹爪的UR5e机器人上，使用SAC算法与JAX框架，在MuJoCo仿真环境中进行了评估，结果表明所提方法能实现鲁棒的装配执行，适用于工业自动化场景。
原文摘要 · Abstract (English)
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.
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