Deep Execution Monitor for Robot Assistive Tasks

Lorenzo Mauro, Edoardo Alati, Marta Sanzari, Valsamis Ntouskos, Gianluca Massimiani, Fiora Pirri

ECCVW 2018 pp. 158-175

doi:10.1007/978-3-030-11024-6_11 /eccvw/2018/mauro2018eccvw-deep/

Abstract

We consider a novel approach to high-level robot task execution for a robot assistive task. In this work we explore the problem of learning to predict the next subtask by introducing a deep model for both sequencing goals and for visually evaluating the state of a task. We show that deep learning for monitoring robot tasks execution very well supports the interconnection between task-level planning and robot operations. These solutions can also cope with the natural non-determinism of the execution monitor. We show that a deep execution monitor leverages robot performance. We measure the improvement taking into account some robot helping tasks performed at a warehouse.

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Text

Mauro et al. "Deep Execution Monitor for Robot Assistive Tasks." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11024-6_11

Markdown

[Mauro et al. "Deep Execution Monitor for Robot Assistive Tasks." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/mauro2018eccvw-deep/) doi:10.1007/978-3-030-11024-6_11

BibTeX

@inproceedings{mauro2018eccvw-deep,
  title     = {{Deep Execution Monitor for Robot Assistive Tasks}},
  author    = {Mauro, Lorenzo and Alati, Edoardo and Sanzari, Marta and Ntouskos, Valsamis and Massimiani, Gianluca and Pirri, Fiora},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2018},
  pages     = {158-175},
  doi       = {10.1007/978-3-030-11024-6_11},
  url       = {https://mlanthology.org/eccvw/2018/mauro2018eccvw-deep/}
}