[en] This paper presents DFL-TORO, a novel Demonstration Framework for Learning Time-Optimal Robotic tasks via One-shot kinesthetic demonstration. It aims at optimizing the process of Learning from Demonstration (LfD), applied in the manufacturing sector. As the effectiveness of LfD is challenged by the quality and efficiency of human demonstrations, our approach offers a streamlined method to intuitively capture task requirements from human teachers, by reducing the need for multiple demonstrations. Furthermore, we propose an optimization-based smoothing algorithm that ensures time-optimal and jerk-regulated demonstration trajectories, while also adhering to the robot’s kinematic constraints. The result is a significant reduction in noise, thereby boosting the robot’s operation efficiency. Evaluations using a Franka Emika Research 3 (FR3) robot for a variety of tasks further substantiate the efficacy of our framework, highlighting its potential to transform kinesthetic demonstrations in manufacturing environments. Moreover, we take our proposed framework into a real manufacturing setting operated by an ABB YuMi robot and showcase its positive impact on LfD outcomes by performing a case study via Dynamic Movement Primitives (DMPs).
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > ARG - Automation & Robotics
Disciplines :
Sciences informatiques
Auteur, co-auteur :
BAREKATAIN, Alireza ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
HABIBI, Hamed ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
VOOS, Holger ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks
Date de publication/diffusion :
octobre 2024
Titre du périodique :
IEEE Access
ISSN :
2169-3536
Maison d'édition :
Institute of Electrical and Electronics Engineers, Etats-Unis - New Jersey
Peer reviewed :
Peer reviewed vérifié par ORBi
Focus Area :
Computational Sciences
Projet FnR :
FNR15882013 - A Combined Machine Learning Approach For The Engineering Of Flexible Assembly Processes Using Collaborative Robots, 2021 (01/04/2021-28/02/2025) - Alireza Barekatain
Intitulé du projet de recherche :
A Combined Machine Learning Approach for the Engineering of Flexible Assembly Processes Using Collaborative Robots (ML-COBOTS)
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