  Audio-visual embodied navigation, as a hot research topic, aims training a
robot to reach an audio target using egocentric visual (from the sensors
mounted on the robot) and audio (emitted from the target) input. The
audio-visual information fusion strategy is naturally important to the
navigation performance, but the state-of-the-art methods still simply
concatenate the visual and audio features, potentially ignoring the direct
impact of context. Moreover, the existing approaches requires either phase-wise
training or additional aid (e.g. topology graph and sound semantics). Up till
this date, the work that deals with the more challenging setup with moving
target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN
(feature self-attention audio-visual navigation) to learn chasing after a
moving audio target using a context-aware audio-visual fusion strategy
implemented as a self-attention module. Our thorough experiments validate the
superior performance (both quantitatively and qualitatively) of FSAAVN in
comparison with the state-of-the-arts, and also provide unique insights about
the choice of visual modalities, visual/audio encoder backbones and fusion
patterns.
