STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
Authors
Carolyn Matl (University of California, Berkeley)*; Yashraj Narang (NVIDIA); Dieter Fox (NVIDIA); Ruzena Bajcsy (UC Berkeley); Fabio Ramos (NVIDIA, The University of Sydney)
Interactive Session
2020-11-17, 11:50 - 12:20 PST | PheedLoop Session
Abstract
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.