  Sound is an information-rich medium that captures dynamic physical events.
This work presents STReSSD, a framework that uses sound to bridge the
simulation-to-reality gap for stochastic dynamics, demonstrated for the
canonical case of a bouncing ball. A physically-motivated noise model is
presented to capture stochastic behavior of the balls upon collision with the
environment. A likelihood-free Bayesian inference framework is used to infer
the parameters of the noise model, as well as a material property called the
coefficient of restitution, from audio observations. The same inference
framework and the calibrated stochastic simulator are then used to learn a
probabilistic model of ball dynamics. The predictive capabilities of the
dynamics model are tested in two robotic experiments. First, open-loop
predictions anticipate probabilistic success of bouncing a ball into a cup. The
second experiment integrates audio perception with a robotic arm to track and
deflect a bouncing ball in real-time. We envision that this work is a step
towards integrating audio-based inference for dynamic robotic tasks.
Experimental results can be viewed at https://youtu.be/b7pOrgZrArk.
