  Language-guided active sensing is a robotics subtask where a robot with an
onboard sensor interacts efficiently with the environment via object
manipulation to maximize perceptual information, following given language
instructions. These tasks appear in various practical robotics applications,
such as household service, search and rescue, and environment monitoring.
Despite many applications, the existing works do not account for language
instructions and have mainly focused on surface sensing, i.e., perceiving the
environment from the outside without rearranging it for dense sensing.
Therefore, in this paper, we introduce the first language-guided active sensing
approach that allows users to observe specific parts of the environment via
object manipulation. Our method spatially associates the environment with
language instructions, determines the best camera viewpoints for perception,
and then iteratively selects and relocates the best view-blocking objects to
provide the dense perception of the region of interest. We evaluate our method
against different baseline algorithms in simulation and also demonstrate it in
real-world confined cabinet-like settings with multiple unknown objects. Our
results show that the proposed method exhibits better performance across
different metrics and successfully generalizes to real-world complex scenarios.
