论文标题
推断颗粒介质的物质特性用于机器人任务
Inferring the Material Properties of Granular Media for Robotic Tasks
论文作者
论文摘要
颗粒状培养基（例如谷物谷物，塑料树脂颗粒和药丸）在机器人集成的行业中无处不在，例如农业，制造业和药物开发。这种流行率要求对这些材料进行准确有效的模拟。这项工作提出了一个软件和硬件框架，该框架会自动校准快速的物理模拟器，以通过从现实世界深度图像颗粒状地层的现实深度图像（即桩和戒指）中推断出材料特性来准确模拟颗粒材料。具体而言，从使用无可能的贝叶斯推断的谷物形成的摘要统计数据中估计了滑动摩擦，滚动摩擦和恢复晶粒的系数。校准的模拟器准确地预测了模拟和实验中看不见的颗粒状地层。此外，显示模拟器预测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上查看框架和实验的可视化。
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
