Ricerc@Sapienza

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.