通过分析多种成功阻抗策略，为机器人插孔装配提供可复用的控制方法。
Divide et Impera: Decoding Impedance Strategies for Robotic Peg-in-Hole Assembly
- 基于子动作与阻抗结合的框架，分解复杂接触任务
- 发现四类不同插头对应的特定与通用装配策略
- 用神经网络预测成功率，降低调试成本
本文研究基于基础动态动作（EDA）框架的机器人插孔装配，该框架通过子动作、振荡与机械阻抗的组合来建模高接触任务。不同于寻找单一最优参数，本文分析多个成功阻抗解的分布与结构，揭示指导阻抗选择的模式。在真实机器人上对四种不同插头进行实验，通过K-means聚类识别出任务特定与通用装配策略。主成分分析（PCA）用于表征这些模式，突出成功阻抗选择的规律。此外，基于神经网络的成功预测器能准确估计可行阻抗参数，减少试错调参需求。通过公开代码、CAD文件及训练模型，本工作提升阻抗控制可及性，为经验较少用户提供建构化的机器人装配编程方案。
原文摘要 · Abstract (English)
This paper investigates robotic peg-in-hole assembly using the Elementary Dynamic Actions (EDA) framework, which models contact-rich tasks through a combination of submovements, oscillations, and mechanical impedance. Rather than focusing on a single optimal parameter set, we analyze the distribution and structure of multiple successful impedance solutions, revealing patterns that guide impedance selection in contactrich robotic manipulation. Experiments with a real robot and four different peg types demonstrate the presence of task-specific and generalized assembly strategies, identified through K-means Clustering. Principal Component Analysis (PCA) is used to represent these findings, highlighting patterns in successful impedance selections. Additionally, a neural-network-based success predictor accurately estimates feasible impedance parameters, reducing the need for extensive trial-and-error tuning. By providing publicly available code, CAD files, and a trained model, this work enhances the accessibility of impedance control and offers a structured approach to programming robotic assembly tasks, particularly for less-experienced users.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。