外观
论文arXiv 2610.2003214 天前
一种带有降维子空间信赖域保护的粒子群优化方法用于富接触控制器调参
原文链接：A particle swarm optimization with a reduced-subspace trust-region safeguard for contact-rich controller tuning（arXiv 2610.20032 · 发布日期：2026-10-14）
A particle swarm optimization with a reduced-subspace trust-region safeguard for contact-rich controller tuning
提出结合PSO探索与TR局部细化的PSO+TR，用于无导数优化可解释插入控制器，并在BCP基准和合成平面插入任务验证。
核心要点
- 富接触插入受对准、摩擦和接触不确定性影响，需兼顾性能、鲁棒性与意外接触恢复。
- PSO+TR在PSO搜索停滞时引入信赖域局部细化，联合调节运动、反馈和恢复参数。
- 在93个BCP问题上，PSO+TR于279个实例中达标228个，为五种方法中最高。
- 合成平面插入中，该流程在开发条件完成率97.78%，压力条件58.00%，并降低压力违规率。
验证范围与局限：验证范围包括BCP基准和合成平面插入任务；合成平面结果仍需物理机器人验证。
英文摘要 (Abstract)
Contact-rich robotic insertion is challenging because uncertainty in alignment, friction, and contact conditions can lead to unsuccessful assembly, excessive contact forces, and jamming. Designing a reliable controller therefore requires balancing insertion performance, robustness, and recovery from unexpected contact. In this paper, we propose PSO+TR, a derivative-free optimization (DFO) method that combines the exploratory search of particle swarm optimization (PSO) with a trust-region (TR) procedure for local refinement to improve the search process when progress stagnates. We apply the method to derivative-free policy optimization of an interpretable robotic insertion controller, jointly tuning its motion, feedback, and recovery parameters while preserving its predefined control structure. The proposed approach is evaluated on 93 eligible benchmark problems from the bound-constrained programs (BCP) collection and a synthetic contact-rich planar insertion task under uncertain operating conditions. Three random seeds per benchmark problem yield 279 matched problem--seed instances per method. PSO+TR reaches the prescribed target on 228 of these instances (81.72%), the highest observed target-attainment rate among the five compared methods, which also include PSO, randomized black-box optimization (VRBBO), matrix adaptation evolution strategy (MAES), and differential evolution (DE). Across eight robotic optimizer seeds, the deployed controller-selection pipeline achieves 97.78% completion under development conditions and 58.00% under withheld stress conditions, compared with 91.22% and 31.12%, respectively, for the fixed staged controller. The stress violation rate is reduced from 20.69% to 2.19%. These findings support combining global and local search for DFO and structured contact-rich controller tuning. The synthetic planar results require validation on physical robots.
作者：Morteza Kimiaei, Roya Khalili-Amirabadi, Mohsen Jalaeian-F.
来源
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