Synthetic Dataset Generation and Learning From Demonstration Applied to Industrial Manipulation
<|reference_start|>Synthetic Dataset Generation and Learning From Demonstration Applied to Industrial Manipulation: 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.<|reference_end|>
@article{barekatain2024synthetic,
    title={Synthetic Dataset Generation and Learning From Demonstration Applied to
  Industrial Manipulation},
    author={Alireza Barekatain, Hamed Rahimi Nohooji, Holger Voos},
    journal={arXiv preprint arXiv:2404.00447},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00447},
    primaryClass={cs.RO}
}