  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.
