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.
Deep execution monitor for robot assistive tasks / Mauro, Lorenzo; Alati, Edoardo; Sanzari, Marta; Ntouskos, Valsamis; Gluca, Massimiani; Fiora, Pirri. - 11134:6(2018), pp. 158-175. (Intervento presentato al convegno 15th European Conference on Computer Vision, ECCV 2018 tenutosi a Munich; Germany) [10.1007/978-3-030-11024-6].
Deep execution monitor for robot assistive tasks
MAURO, LORENZO;Edoardo Alati;Marta Sanzari1;Valsamis Ntouskos;Fiora Pirri
2018
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.File | Dimensione | Formato | |
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Mauro_Postprint_Deep-execution-monitor_2018.pdf
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Note: https://link.springer.com/chapter/10.1007/978-3-030-11024-6_11
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