ML Anthology

Vision Augmented Robot Feeding

Alexandre Candeias, Travers Rhodes, Manuel Marques, João Paulo Costeira, Manuela Veloso
ECCVW 2018 pp. 50-65
doi:10.1007/978-3-030-11024-6_4 /eccvw/2018/candeias2018eccvw-vision/

Abstract

Researchers have over time developed robotic feeding assistants to help at meals so that people with disabilities can live more autonomous lives. Current commercial feeding assistant robots acquire food without feedback on acquisition success and move to a preprogrammed location to deliver the food. In this work, we evaluate how vision can be used to improve both food acquisition and delivery. We show that using visual feedback on whether food was captured increases food acquisition efficiency. We also show how Discriminative Optimization (DO) can be used in tracking so that the food can be effectively brought all the way to the user’s mouth, rather than to a preprogrammed feeding location.

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Cite

Text

Candeias et al. "Vision Augmented Robot Feeding." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11024-6_4

Markdown

[Candeias et al. "Vision Augmented Robot Feeding." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/candeias2018eccvw-vision/) doi:10.1007/978-3-030-11024-6_4

BibTeX

@inproceedings{candeias2018eccvw-vision,
  title     = {{Vision Augmented Robot Feeding}},
  author    = {Candeias, Alexandre and Rhodes, Travers and Marques, Manuel and Costeira, João Paulo and Veloso, Manuela},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2018},
  pages     = {50-65},
  doi       = {10.1007/978-3-030-11024-6_4},
  url       = {https://mlanthology.org/eccvw/2018/candeias2018eccvw-vision/}
}