Granular materials (GMs) are ubiquitous in daily life. Understanding their properties is also important, especially in agriculture and industry. However, existing works require dedicated measurement equipment and also need large human efforts to handle a large number of particles. In this paper, we introduce a method for estimating the relative values of particle size and density from the video of the interaction with GMs. It is trained on a visuo-haptic learning framework inspired by a contact model, which reveals the strong correlation between GM properties and the visual-haptic data during the probe-dragging in the GMs. After training, the network can map the visual modality well to the haptic signal and implicitly characterize the relative distribution of particle properties in its latent embeddings, as interpreted in that contact model. Therefore, we can analyze GM properties using the trained encoder, and only visual information is needed without extra sensory modalities and human efforts for labeling. The presented GM property estimator has been extensively validated via comparison and ablation experiments. The generalization capability has also been evaluated and a real-world application on the beach is also demonstrated. Experiment videos are available at \url{https://sites.google.com/view/gmwork/vhlearning} .

翻译：颗粒材料在日常生活中无处不在。理解其属性在农业和工业领域尤为重要。然而，现有方法需要专门的测量设备，且处理大量颗粒时需耗费大量人力。本文提出一种方法，可通过颗粒材料交互视频估计颗粒尺寸与密度的相对值。该方法基于受接触模型启发的视觉-触觉学习框架进行训练，该框架揭示了探针在颗粒材料中拖拽时，材料属性与视觉-触觉数据间的强相关性。训练完成后，网络能够将视觉模态良好映射至触觉信号，并在其潜在嵌入中隐式表征颗粒属性的相对分布，这与接触模型的解释一致。因此，我们可使用训练好的编码器分析颗粒材料属性，仅需视觉信息而无需额外传感模态或人工标注。所提出的颗粒材料属性估计器已通过对比实验与消融实验得到广泛验证。其泛化能力亦经过评估，并在海滩场景中展示了实际应用效果。实验视频详见 \url{https://sites.google.com/view/gmwork/vhlearning}。