User-Driven Learning from Demonstration: A Trajectory and Impedance Learning Method
Robotics
Summary
The authors developed a method to help robots learn tasks quickly and safely from just one demonstration, without needing repeated teaching. Their method allows the robot to copy motions precisely while adjusting its stiffness in real time to be both flexible and stable when humans interact with it. They combined advanced math tools and filters to correct errors and keep smooth contact during tasks. The method was successfully tested on a 7-joint KUKA robot arm.
Authors
Zi-Qi Yang, Mehrdad R. Kermani
Abstract
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.