  Learning to produce contact-rich, dynamic behaviors from raw sensory data has
been a longstanding challenge in robotics. Prominent approaches primarily focus
on using visual or tactile sensing, where unfortunately one fails to capture
high-frequency interaction, while the other can be too delicate for large-scale
data collection. In this work, we propose a data-centric approach to dynamic
manipulation that uses an often ignored source of information: sound. We first
collect a dataset of 25k interaction-sound pairs across five dynamic tasks
using commodity contact microphones. Then, given this data, we leverage
self-supervised learning to accelerate behavior prediction from sound. Our
experiments indicate that this self-supervised 'pretraining' is crucial to
achieving high performance, with a 34.5% lower MSE than plain supervised
learning and a 54.3% lower MSE over visual training. Importantly, we find that
when asked to generate desired sound profiles, online rollouts of our models on
a UR10 robot can produce dynamic behavior that achieves an average of 11.5%
improvement over supervised learning on audio similarity metrics.
