  Deep robot vision models are widely used for recognizing objects from camera
images, but shows poor performance when detecting objects at untrained
positions. Although such problem can be alleviated by training with large
datasets, the dataset collection cost cannot be ignored. Existing visual
attention models tackled the problem by employing a data efficient structure
which learns to extract task relevant image areas. However, since the models
cannot modify attention targets after training, it is difficult to apply to
dynamically changing tasks. This paper proposed a novel Key-Query-Value
formulated visual attention model. This model is capable of switching attention
targets by externally modifying the Query representations, namely top-down
attention. The proposed model is experimented on a simulator and a real-world
environment. The model was compared to existing end-to-end robot vision models
in the simulator experiments, showing higher performance and data efficiency.
In the real-world robot experiments, the model showed high precision along with
its scalability and extendibility.
