  Several recent studies have demonstrated the promise of deep visuomotor
policies for robot manipulator control. Despite impressive progress, these
systems are known to be vulnerable to physical disturbances, such as accidental
or adversarial bumps that make them drop the manipulated object. They also tend
to be distracted by visual disturbances such as objects moving in the robot's
field of view, even if the disturbance does not physically prevent the
execution of the task. In this paper, we propose an approach for augmenting a
deep visuomotor policy trained through demonstrations with Task Focused visual
Attention (TFA). The manipulation task is specified with a natural language
text such as `move the red bowl to the left'. This allows the visual attention
component to concentrate on the current object that the robot needs to
manipulate. We show that even in benign environments, the TFA allows the policy
to consistently outperform a variant with no attention mechanism. More
importantly, the new policy is significantly more robust: it regularly recovers
from severe physical disturbances (such as bumps causing it to drop the object)
from which the baseline policy, i.e. with no visual attention, almost never
recovers. In addition, we show that the proposed policy performs correctly in
the presence of a wide class of visual disturbances, exhibiting a behavior
reminiscent of human selective visual attention experiments. Our proposed
approach consists of a VAE-GAN network which encodes the visual input and feeds
it to a Motor network that moves the robot joints. Also, our approach benefits
from a teacher network for the TFA that leverages textual input command to
robustify the visual encoder against various types of disturbances.
