This paper presents a system for generating Gaussian path models from teaching data representing the path shape. In addition, methods for using these path models to classify human demonstrations of paths are introduced. By generating a library of multiple Gaussian path models of various shapes, human demonstrations can be used for intuitive robot motion programming. A method for modifying existing Gaussian path models by demonstration through geometric analysis is also presented.
@article{arxiv.2509.10007,
title = {Gaussian path model library for intuitive robot motion programming by demonstration},
author = {Samuli Soutukorva and Markku Suomalainen and Martin Kollingbaum and Tapio Heikkilä},
journal= {arXiv preprint arXiv:2509.10007},
year = {2025}
}