  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/.
