  In audio-visual navigation (AVN), an intelligent agent needs to navigate to a
constantly sound-making object in complex 3D environments based on its audio
and visual perceptions. While existing methods attempt to improve the
navigation performance with preciously designed path planning or intricate task
settings, none has improved the model generalisation on unheard sounds with
task settings unchanged. We thus propose a contrastive learning-based method to
tackle this challenge by regularising the audio encoder, where the
sound-agnostic goal-driven latent representations can be learnt from various
audio signals of different classes. In addition, we consider two data
augmentation strategies to enrich the training sounds. We demonstrate that our
designs can be easily equipped to existing AVN frameworks to obtain an
immediate performance gain (13.4%$\uparrow$ in SPL on Replica and
12.2%$\uparrow$ in SPL on MP3D). Our project is available at
https://AV-GeN.github.io/.
