发表机构
Department of Electrical and Computer Engineering, Western University（西安大略大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种用户驱动的机器人示范学习方法，结合三维快速微分同胚匹配算法与基于动态系统的运动生成器，利用扩展卡尔曼滤波器补偿误差，引入阻抗参数化函数学习阻抗变化，经实验验证可减少用户工作量，确保动作精确再现并提升人机交互安全性。
AI 中文摘要
本文提出了一种用于用户驱动的机器人示范学习（LfD）的方法，该方法在确保柔顺和精确再现的同时减少了用户的工作量。该方法消除了对同一任务的重复示教，并能从单个示范中进行实时学习。示范动作能高精度再现，同时实时学习阻抗变化以提供柔顺性和抗干扰鲁棒性，减轻了传统缺乏柔顺性的时间索引轨迹在人机交互中产生的潜在安全问题。该方法将三维快速微分同胚匹配（FDM）算法与基于动态系统（DS）的运动生成器相结合，实现实时单次示范学习和再现。扩展卡尔曼滤波器（EKF）框架补偿再现误差并从外部交互中恢复。此外，还引入了阻抗参数化函数来从示范中学习阻抗变化并在特定应用中保持表面接触。通过在7自由度的库卡LWR IV +机器人上进行的综合实验验证了该方法。
英文摘要
This paper presents a method for user-driven robot Learning from Demonstration (LfD) that reduces user effort while ensuring compliant and precise reproduction. The method eliminates repeated teaching for the same task and enables real-time learning from a single demonstration. Demonstrated motions are reproduced with high precision, while impedance variations are learned in real time to provide both compliance and robustness against perturbations. This mitigates potential safety issues in Human-Robot Interaction (HRI) that arise from conventional time-indexed trajectories lacking compliance. The proposed approach integrates a three-dimensional (3D) Fast Diffeomorphic Matching (FDM) algorithm with a Dynamical System (DS)-based motion generator to achieve real-time single-shot demonstration learning and reproduction. An Extended Kalman Filter (EKF) framework compensates for reproduction errors and recovers from external interactions. Furthermore, an impedance parameterization function is incorporated to learn impedance variations from demonstrations and maintain surface contact for specific applications. The proposed approach is validated through comprehensive experiments on a 7 Degree-of-Freedom (DOF) KUKA LWR IV+ robot.
CommentsAccepted at IEEE International Conference on Automation Science and Engineering (CASE) 2026, 7 pages
