通过力感知估算插销偏差，提升机器人装配成功率。
Force-Based Offset Estimation for Keyed Peg-in-Hole Assembly Using Local Gaussian Process Regression
- 用局部KNN-GP混合模型从力矩数据直接估计偏移量。
- 在倒角插入场景下，装配成功率达87%（原67%）。
- 适合对精度要求高的工业装配任务，尤其复杂环境。
键槽插销装配任务对几何约束要求严格，在不确定环境下对抓取姿态偏差敏感。本文提出一种基于力的偏移估计算法，嵌入于感知-验证-插入流程中。利用腕部力/力矩传感器测量值，通过局部KNN-Gaussian Process混合回归器直接估计残余错位。框架区分硬碰撞与引导倒角插入两种接触状态，并为每种状态分配专用模型；通过接触窗口持续时间阈值实现状态分类。结合抓取后单目视觉验证结果的确定性搜索，提升了回归模型精度。该方法在基于关键点检测的抓取放置应用中实现了倒角插销的高精度径向偏移估计。实验使用协作机械臂集成的力/力矩传感器，应用该流程后插入成功率由67%提升至87%。
原文摘要 · Abstract (English)
Key-keyway assembly tasks impose strict geometric constraints and are highly sensitive to grasp pose deviations in uncertain environments. This work presents a force-based offset estimation method for keyed peg-in-hole assembly, embedded within a perception-validation-insertion pipeline. Residual misalignment is estimated directly from wrist force/torque measurements using a local KNN-Gaussian Process hybrid regressor. The framework distinguishes between two contact regimes, hard collision and guided chamfer insertion, and routes inference to a dedicated model for each. Regime classification is achieved via a contact-window duration threshold. KNN combined with a deterministic search using the results of a post-grasp monocular visual validation contributes to an increased accuracy of the regressor model. This approach achieves accurate radial offset estimation in chamfered peg insertion, during a keypoint detection-based pick and place application. Experiments using the integrated force/torque sensor of a collaborative robot arm showed an increase in insertion success rate from 67% to 87% after the pipeline was applied.
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