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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 betweentask-level planning and robot operations. These solutions can also cope with thenatural non-determinism of the execution monitor. We show that a deep executionmonitor leverages robot performance. We measure the improvement taking intoaccount some robot helping tasks performed at a warehouse.

 


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