  The ability to simulate and predict the outcome of contacts is paramount to
the successful execution of many robotic tasks. Simulators are powerful tools
for the design of robots and their behaviors, yet the discrepancy between their
predictions and observed data limit their usability. In this paper, we propose
a self-supervised approach to learning residual models for rigid-body
simulators that exploits corrections of contact models to refine predictive
performance and propagate uncertainty. We empirically evaluate the framework by
predicting the outcomes of planar dice rolls and compare it's performance to
state-of-the-art techniques.
