  Language is an effective medium for bi-directional communication in
human-robot teams. To infer the meaning of many instructions, robots need to
construct a model of their surroundings that describe the spatial, semantic,
and metric properties of objects from observations and prior information about
the environment. Recent algorithms condition the expression of object detectors
in a robot's perception pipeline on language to generate a minimal
representation of the environment necessary to efficiently determine the
meaning of the instruction. We expand on this work by introducing the ability
to express hierarchies between detectors. This assists in the development of
environment models suitable for more sophisticated tasks that may require
modeling of kinematics, dynamics, and/or affordances between objects. To
achieve this, a novel extension of symbolic representations for language-guided
adaptive perception is proposed that reasons over single-layer object detector
hierarchies. Differences in perception performance and environment
representations between adaptive perception and a suitable exhaustive baseline
are explored through physical experiments on a mobile manipulator.
