Deep execution monitor for robot assistive tasks

Lorenzo Mauro, Edoardo Alati, Marta Sanzari, Valsamis Ntouskos, Gianluca Massimiani, Fiora Pirri; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Mauro_2018_ECCV_Workshops,
author = {Mauro, Lorenzo and Alati, Edoardo and Sanzari, Marta and Ntouskos, Valsamis and Massimiani, Gianluca and Pirri, Fiora},
title = {Deep execution monitor for robot assistive tasks},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}