利用力测量交互式识别颗粒材料
Interactive Identification of Granular Materials using Force Measurements
AI总结：
针对现有颗粒材料识别方法依赖容器摇晃的局限，提出仅靠力-力矩测量、通过直接交互识别颗粒材料的框架，结合时域与频域特征构建高性能特征空间，在11种材料的数据集上实现近乎完美的识别准确率，且数据集与代码已开源。
摘要：
尽管颗粒材料识别能力在机器人烹饪、土方工程等应用中颇具潜力，但该领域仍存在诸多挑战，现有方法大多依赖在密闭容器中摇晃材料。本研究提出一种交互式材料识别框架，使机器人仅通过力-力矩测量即可识别多种颗粒材料。与已有研究不同，该方法采用与材料直接交互的方式。研究通过包含11种颗粒材料的真实世界数据集开展实验评估，该数据集也已公开发布。结果表明，仅依靠直接交互获取的力测量值，本方法就能以近乎完美的准确率识别多种颗粒材料。此外，全面的数据分析与实验显示，高性能特征空间必须结合力信号时域动力学与频谱相关特征。为此，研究提出将原始信号及其高频幅值直方图相结合，作为推荐的特征空间表示。实验证明，该特征空间的性能显著优于基线方法。代码与数据集可访问：https://irobotics.aalto.fi/identify_granular/。
英文摘要：
Despite the potential the ability to identify granular materials creates for applications such as robotic cooking or earthmoving, granular material identification remains a challenging area, existing methods mostly relying on shaking the materials in closed containers. This work presents an interactive material identification framework that enables robots to identify a wide range of granular materials using only force-torque measurements. Unlike prior works, the proposed approach uses direct interaction with the materials. The approach is evaluated through experiments with a real-world dataset comprising 11 granular materials, which we also make publicly available. Results show that our method can identify a wide range of granular materials with near-perfect accuracy while relying solely on force measurements obtained from direct interaction. Further, our comprehensive data analysis and experiments show that a high-performancefeature space must combine features related to the force signal's time-domain dynamics and frequency spectrum. We account for this by proposing a combination of the raw signal and its high-frequency magnitude histogram as the suggested feature space representation. We show that the proposed feature space outperforms baselines by a significant margin. The code and data set are available at: https://irobotics.aalto.fi/identify_granular/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
