# User-Driven Learning from Demonstration: A Trajectory and Impedance Learning Method

arXiv: https://arxiv.org/abs/2607.16998 · HTML: https://mlsanity.com/paper/2607.16998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 4 of 4)

- [ILoSA: Interactive Learning of Stiffness and Attractors](https://mlsanity.com/paper/2103.03099/) (2021)
- [Quantifying Demonstration Quality for Robot Learning and Generalization](https://mlsanity.com/paper/2203.13843/) (2022)
- [A Null Space Compliance Approach for Maintaining Safety and Tracking Performance in Human-Robot Interactions](https://mlsanity.com/paper/2502.02443/) (2025)
- [A Unified Multi-Layer Framework for Skill Acquisition from Imperfect Human Demonstrations](https://mlsanity.com/paper/2604.08341/) (2026)

## Abstract (arXiv)

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.