TEAM: A Parameter-Free Algorithm to Teach Collaborative Robots Motions from User Demonstrations

Lorenzo Panchetti, Jianhao Zheng, Mohamed Bouri, Malcolm Mielle

2023

Abstract

Learning from demonstrations (LfD) enables humans to easily teach collaborative robots (cobots) new motions that can be generalized to new task configurations without retraining. However, state-of-the-art LfD methods require manually tuning intrinsic parameters and have rarely been used in industrial contexts without experts. We propose a parameter-free LfD method based on probabilistic movement primitives, where parameters are determined using Jensen-Shannon divergence and Bayesian optimization, and users do not have to perform manual parameter tuning. The cobot’s precision in reproducing learned motions, and its ease of teaching and use by non-expert users are evaluated in two field tests. In the first field test, the cobot works on elevator door maintenance. In the second test, three factory workers teach the cobot tasks useful for their daily workflow. Errors between the cobot and target joint angles are insignificant—at worst 0.28 deg—and the motion is accurately reproduced—GMCC score of 1. Questionnaires completed by the workers highlighted the method’s ease of use and the accuracy of the reproduced motion. Public implementation of our method and datasets are made available online.

Paper Citation

in Harvard Style

Panchetti L., Zheng J., Bouri M. and Mielle M. (2023). TEAM: A Parameter-Free Algorithm to Teach Collaborative Robots Motions from User Demonstrations. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 570-577. DOI: 10.5220/0012159700003543

in Bibtex Style

@conference{icinco23,
author={Lorenzo Panchetti and Jianhao Zheng and Mohamed Bouri and Malcolm Mielle},
title={TEAM: A Parameter-Free Algorithm to Teach Collaborative Robots Motions from User Demonstrations},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={570-577},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012159700003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - TEAM: A Parameter-Free Algorithm to Teach Collaborative Robots Motions from User Demonstrations
SN - 978-989-758-670-5
AU - Panchetti L.
AU - Zheng J.
AU - Bouri M.
AU - Mielle M.
PY - 2023
SP - 570
EP - 577
DO - 10.5220/0012159700003543
PB - SciTePress