用户只需一次演示，机器人就能精准复现动作并实时学习柔顺性。
User-Driven Learning from Demonstration: A Trajectory and Impedance Learning Method
- 结合3D快速微分匹配与动力系统生成，实现单次演示实时学习。
- 在7自由度机械臂上实现高精度动作复现与抗扰动柔顺控制。
- 适合需要安全交互的工业装配、医疗操作等场景。
本文提出一种用户驱动的机器人示教学习方法，通过单次演示即可实现动作的高精度复现与实时柔顺性学习，显著降低用户教学负担。该方法摒弃传统时间对齐轨迹，采用三维快速微分匹配（3D FDM）算法与基于动力系统的运动生成器，实现实时单次演示学习。同时引入扩展卡尔曼滤波（EKF）框架补偿复现误差并恢复外部干扰。通过阻抗参数化函数，从示范中学习阻抗变化，在特定应用中维持表面接触。在7自由度的KUKA LWR IV+机器人上进行了全面实验验证，结果表明该方法能有效提升人机协作中的安全性与鲁棒性。
原文摘要 · Abstract (English)
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.
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