| Autor: |
Mauro, Lorenzo, Alati, Edoardo, Sanzari, Marta, Ntouskos, Valsamis, Massimiani, Gianluca, Pirri, Fiora |
| Rok vydání: |
2019 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/978-3-030-11024-6_11 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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