  An environment acoustic model represents how sound is transformed by the
physical characteristics of an indoor environment, for any given
source/receiver location. Traditional methods for constructing acoustic models
involve expensive and time-consuming collection of large quantities of acoustic
data at dense spatial locations in the space, or rely on privileged knowledge
of scene geometry to intelligently select acoustic data sampling locations. We
propose active acoustic sampling, a new task for efficiently building an
environment acoustic model of an unmapped environment in which a mobile agent
equipped with visual and acoustic sensors jointly constructs the environment
acoustic model and the occupancy map on-the-fly. We introduce ActiveRIR, a
reinforcement learning (RL) policy that leverages information from audio-visual
sensor streams to guide agent navigation and determine optimal acoustic data
sampling positions, yielding a high quality acoustic model of the environment
from a minimal set of acoustic samples. We train our policy with a novel RL
reward based on information gain in the environment acoustic model. Evaluating
on diverse unseen indoor environments from a state-of-the-art acoustic
simulation platform, ActiveRIR outperforms an array of methods--both
traditional navigation agents based on spatial novelty and visual exploration
as well as existing state-of-the-art methods.
