  The classical human-robot interface in uncalibrated image-based visual
servoing (UIBVS) relies on either human annotations or semantic segmentation
with categorical labels. Both methods fail to match natural human communication
and convey rich semantics in manipulation tasks as effectively as natural
language expressions. In this paper, we tackle this problem by using referring
expression segmentation, which is a prompt-based approach, to provide more
in-depth information for robot perception. To generate high-quality
segmentation predictions from referring expressions, we propose CLIPUNetr - a
new CLIP-driven referring expression segmentation network. CLIPUNetr leverages
CLIP's strong vision-language representations to segment regions from referring
expressions, while utilizing its ``U-shaped'' encoder-decoder architecture to
generate predictions with sharper boundaries and finer structures. Furthermore,
we propose a new pipeline to integrate CLIPUNetr into UIBVS and apply it to
control robots in real-world environments. In experiments, our method improves
boundary and structure measurements by an average of 120% and can successfully
assist real-world UIBVS control in an unstructured manipulation environment.
