  Granular media (e.g., cereal grains, plastic resin pellets, and pills) are
ubiquitous in robotics-integrated industries, such as agriculture,
manufacturing, and pharmaceutical development. This prevalence mandates the
accurate and efficient simulation of these materials. This work presents a
software and hardware framework that automatically calibrates a fast physics
simulator to accurately simulate granular materials by inferring material
properties from real-world depth images of granular formations (i.e., piles and
rings). Specifically, coefficients of sliding friction, rolling friction, and
restitution of grains are estimated from summary statistics of grain formations
using likelihood-free Bayesian inference. The calibrated simulator accurately
predicts unseen granular formations in both simulation and experiment;
furthermore, simulator predictions are shown to generalize to more complex
tasks, including using a robot to pour grains into a bowl, as well as to create
a desired pattern of piles and rings. Visualizations of the framework and
experiments can be viewed at https://youtu.be/OBvV5h2NMKA
