Cite this DOI

MidhunM. S, M. S. (2022). Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions. *Journal of Science & Technology*, *7*(4), 136–142. https://doi.org/10.46243/jst.2022.v7.i04.pp136-142

@article{midhunms2022triplet,
  author    = {MidhunM. S, MidhunM. S},
  title     = {{Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {jun},
  volume    = {7},
  number    = {4},
  pages     = {136--142},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i04.pp136-142},
  url       = {https://doi.org/10.46243/jst.2022.v7.i04.pp136-142},
  language  = {en},
  abstract  = {Human/systems managing the robot may make errors, resulting in a significant loss.A robotic task outlier identification approach for serial manipulator setups to avoid such outliers. The suggested work generates robot tasks in the first stage by recording the joint valu es utilised as the proposed dataset. Then, the metric learning-based Triplet model with convolutional feature learning layers is presented for few -shot feature learning in robot tasks. Each job is represented by an n -dimensional vector, where n represents the robot's degrees of freedom. The robot work comprises about 1500 characters from the Omniglot dataset; therefore, drawing characters from several languages on a canvas is chosen as a test}
}

TY  - JOUR
TI  - Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions
AU  - MidhunM. S, MidhunM. S
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/06/30/
VL  - 7
IS  - 4
SP  - 136
EP  - 142
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Human/systems managing the robot may make errors, resulting in a significant loss.A robotic task outlier identification approach for serial manipulator setups to avoid such outliers. The suggested work generates robot tasks in the first stage by recording the joint valu es utilised as the proposed dataset. Then, the metric learning-based Triplet model with convolutional feature learning layers is presented for few -shot feature learning in robot tasks. Each job is represented by an n -dimensional vector, where n represents the robot's degrees of freedom. The robot work comprises about 1500 characters from the Omniglot dataset; therefore, drawing characters from several languages on a canvas is chosen as a test
DO  - 10.46243/jst.2022.v7.i04.pp136-142
UR  - https://doi.org/10.46243/jst.2022.v7.i04.pp136-142
ER  -

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    "URL": "https://doi.org/10.46243/jst.2022.v7.i04.pp136-142",
    "title": "Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions",
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    "language": "en",
    "abstract": "Human/systems managing the robot may make errors, resulting in a significant loss.A robotic task outlier identification approach for serial manipulator setups to avoid such outliers. The suggested work generates robot tasks in the first stage by recording the joint valu es utilised as the proposed dataset. Then, the metric learning-based Triplet model with convolutional feature learning layers is presented for few -shot feature learning in robot tasks. Each job is represented by an n -dimensional vector, where n represents the robot's degrees of freedom. The robot work comprises about 1500 characters from the Omniglot dataset; therefore, drawing characters from several languages on a canvas is chosen as a test",
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i04.pp136-142 gives all four in one JSON answer.