Name
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. Experimental results can be viewed at https://youtu.be/b7pOrgZrArk. |
Year | Venue | DocType |
|---|---|---|
2020 | CoRL | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carolyn Matl | 1 | 3 | 1.75 |
| Yashraj Narang | 2 | 0 | 0.34 |
| Dieter Fox | 3 | 12306 | 1289.74 |
| Ruzena Bajcsy | 4 | 3621 | 869.56 |
| Fabio Ramos | 5 | 733 | 73.91 |