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

Inferring the Material Properties of Granular Media for Robotic Tasks