The ability to identify granular materials facilitates the emergence of various new applications in robotics, ranging from cooking at home to truck loading at mining sites. However, granular material identification remains a challenging and underexplored area. In this work, we present a novel interactive material identification framework that enables robots to identify a wide range of granular materials using only a force-torque sensor for perception. Our framework, comprising interactive exploration, feature extraction, and classification stages, prioritizes simplicity and transparency for seamless integration into various manipulation pipelines. We evaluate the proposed approach through extensive experiments with a real-world dataset comprising 11 granular materials, which we also make publicly available. Additionally, we conducted a comprehensive qualitative analysis of the dataset to offer deeper insights into its nature, aiding future development. Our results show that the proposed method is capable of accurately identifying a wide range of granular materials solely relying on force measurements obtained from direct interaction with the materials. Code and dataset are available at: https://irobotics.aalto.fi/indentify_granular/.

翻译：颗粒材料的识别能力有助于促进机器人领域新兴应用的发展，涵盖从家庭烹饪到采矿场卡车装载等场景。然而，颗粒材料识别仍是一个具有挑战性且尚未充分探索的研究领域。本文提出一种新颖的交互式材料识别框架，使机器人仅需通过力/力矩传感器感知即可识别多种颗粒材料。该框架包含交互式探索、特征提取与分类三个模块，重点强调简洁性与可解释性，便于无缝集成至各类操作流程中。我们通过包含11种颗粒材料的真实世界数据集（该数据集已公开）开展大量实验评估所提方法。此外，我们对数据集进行了全面的定性分析，以揭示其内在特性，为后续研究提供支撑。实验结果表明，所提方法能够仅依靠与材料直接交互获得的力测量值，准确识别多种颗粒材料。代码与数据集已开源：https://irobotics.aalto.fi/indentify_granular/