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arXiv · 1902.02877

Deep execution monitor for robot assistive tasks

Lorenzo Mauro·Edoardo Alati·Marta Sanzari·Valsamis Ntouskos·Gianluca Massimiani·Fiora Pirri
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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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Lorenzo Mauro, Edoardo Alati, Marta Sanzari, Valsamis Ntouskos, Gianluca Massimiani, Fiora Pirri. 2019-02-07. Deep execution monitor for robot assistive tasks. https://doi.org/10.1007/978-3-030-11024-6_11

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