Execution monitor of high-level robot actions can be effectively improved by
visual monitoring the state of the world in terms of preconditions and
postconditions that hold before and after the execution of an action.
Furthermore a policy for searching where to look at, either for verifying the
relations that specify the pre and postconditions or to refocus in case of a
failure, can tremendously improve the robot execution in an uncharted
environment. It is now possible to strongly rely on visual perception in order
to make the assumption that the environment is observable, by the amazing
results of deep learning. In this work we present visual execution monitoring
for a robot executing tasks in an uncharted Lab environment. The execution
monitor interacts with the environment via a visual stream that uses two DCNN
for recognizing the objects the robot has to deal with and manipulate, and a
non-parametric Bayes estimation to discover the relations out of the DCNN
features. To recover from lack of focus and failures due to missed objects we
resort to visual search policies via deep reinforcement learning
