DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks
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...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10735215/ |
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| author | Alireza Barekatain Hamed Habibi Holger Voos |
| author_facet | Alireza Barekatain Hamed Habibi Holger Voos |
| author_sort | Alireza Barekatain |
| collection | DOAJ |
| description | 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). |
| format | Article |
| id | doaj-art-d2b5e820505245f8acece6f54457ea8e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d2b5e820505245f8acece6f54457ea8e2025-08-20T02:12:49ZengIEEEIEEE Access2169-35362024-01-011216116416118410.1109/ACCESS.2024.348661510735215DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing TasksAlireza Barekatain0https://orcid.org/0000-0001-5646-2675Hamed Habibi1https://orcid.org/0000-0002-7393-6235Holger Voos2https://orcid.org/0000-0002-9600-8386Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, LuxembourgThis 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).https://ieeexplore.ieee.org/document/10735215/ABB YuMiFranka Emika Research 3imitation learninglearning from demonstrationmanufacturing roboticsrobotic manipulator |
| spellingShingle | Alireza Barekatain Hamed Habibi Holger Voos DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks IEEE Access ABB YuMi Franka Emika Research 3 imitation learning learning from demonstration manufacturing robotics robotic manipulator |
| title | DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks |
| title_full | DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks |
| title_fullStr | DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks |
| title_full_unstemmed | DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks |
| title_short | DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks |
| title_sort | dfl toro a one shot demonstration framework for learning time optimal robotic manufacturing tasks |
| topic | ABB YuMi Franka Emika Research 3 imitation learning learning from demonstration manufacturing robotics robotic manipulator |
| url | https://ieeexplore.ieee.org/document/10735215/ |
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