Deep execution monitor for robot assistive tasks
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 (add twitter)
Edoardo Alati (add twitter)
Marta Sanzari (add twitter)
Valsamis Ntouskos (add twitter)
Gianluca Massimiani (add twitter)
Fiora Pirri (add twitter)
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02/10/19 06:05PM
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arxiv_cscv: Deep execution monitor for robot assistive tasks https://t.co/LXMDtYItp1
arxivml: "Deep execution monitor for robot assistive tasks", Lorenzo Mauro, Edoardo Alati, Marta Sanzari, Valsamis Ntouskos,… https://t.co/tO39KSU5Uu
arxiv_cscv: Deep execution monitor for robot assistive tasks https://t.co/LXMDtYItp1
robotic_hands: RT @arxiv_org: Deep execution monitor for robot assistive tasks. https://t.co/0JaEorbMyi https://t.co/P7B5jQzTEI
arxiv_org: Deep execution monitor for robot assistive tasks. https://t.co/0JaEorbMyi https://t.co/P7B5jQzTEI
arxiv_cscv: Deep execution monitor for robot assistive tasks https://t.co/LXMDtZ04gz
arxiv_cscv: Deep execution monitor for robot assistive tasks https://t.co/LXMDtYItp1
BrundageBot: Deep execution monitor for robot assistive tasks. Lorenzo Mauro, Edoardo Alati, Marta Sanzari, Valsamis Ntouskos, Gianluca Massimiani, and Fiora Pirri https://t.co/9i6Ct4Q9Qh
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