Synthetic Dataset Generation and Learning From Demonstration Applied to Industrial Manipulation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911820700188672 |
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| author | Barekatain, Alireza Nohooji, Hamed Rahimi Voos, Holger |
| author_facet | Barekatain, Alireza Nohooji, Hamed Rahimi Voos, Holger |
| contents | The aim of this study is to investigate an automated industrial manipulation pipeline, where assembly tasks can be flexibly adapted to production without the need for a robotic expert, both for the vision system and the robot program. The objective of this study is first, to develop a synthetic-dataset-generation pipeline with a special focus on industrial parts, and second, to use Learning-from-Demonstration (LfD) methods to replace manual robot programming, so that a non-robotic expert/process engineer can introduce a new manipulation task by teaching it to the robot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00447 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthetic Dataset Generation and Learning From Demonstration Applied to Industrial Manipulation Barekatain, Alireza Nohooji, Hamed Rahimi Voos, Holger Robotics The aim of this study is to investigate an automated industrial manipulation pipeline, where assembly tasks can be flexibly adapted to production without the need for a robotic expert, both for the vision system and the robot program. The objective of this study is first, to develop a synthetic-dataset-generation pipeline with a special focus on industrial parts, and second, to use Learning-from-Demonstration (LfD) methods to replace manual robot programming, so that a non-robotic expert/process engineer can introduce a new manipulation task by teaching it to the robot. |
| title | Synthetic Dataset Generation and Learning From Demonstration Applied to Industrial Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.00447 |