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Sound is one of the most informative and abundant modalities in the realworld while being robust to sense without contacts by small and cheap sensorsthat can be placed on mobile devices. Although deep learning is capable ofextracting information from multiple sensory inputs, there has been little useof sound for the control and learning of robotic actions. For unsupervisedreinforcement learning, an agent is expected to actively collect experiencesand jointly learn representations and policies in a self-supervised way. Webuild realistic robotic manipulation scenarios with physics-based soundsimulation and propose the Intrinsic Sound Curiosity Module (ISCM). The ISCMprovides feedback to a reinforcement learner to learn robust representationsand to reward a more efficient exploration behavior. We perform experimentswith sound enabled during pre-training and disabled during adaptation, and showthat representations learned by ISCM outperform the ones by vision-onlybaselines and pre-trained policies can accelerate the learning process whenapplied to downstream tasks.

Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations