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

中文翻译：

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为机器人任务推断颗粒介质的材料特性

颗粒介质（例如，谷物、塑料树脂颗粒和药丸）在机器人集成行业中无处不在，例如农业、制造业和药物开发。这种流行要求对这些材料进行准确有效的模拟。这项工作提出了一种软件和硬件框架，可以自动校准快速物理模拟​​器，通过从颗粒地层（即桩和环）的真实世界深度图像推断材料特性来准确模拟颗粒材料。具体而言，滑动摩擦系数、滚动摩擦系数和晶粒恢复系数是使用无似然贝叶斯推理从晶粒形成的汇总统计中估计出来的。校准后的模拟器准确预测了模拟和实验中未见的颗粒结构；此外，模拟器预测被证明可以推广到更复杂的任务，包括使用机器人将谷物倒入碗中，以及创建所需的桩和环模式。可以在 https://youtu.be/OBvV5h2NMKA 查看框架和实验的可视化