In haptic object discrimination, the effect of gripper embodiment, action parameters, and sensory channels has not been systematically studied. We used two anthropomorphic hands and two 2-finger grippers to grasp two sets of deformable objects. On the object classification task, we found: (i) among classifiers, SVM on sensory features and LSTM on raw time series performed best across all grippers; (ii) faster compression speeds degraded performance; (iii) generalization to different grasping configurations was limited; transfer to different compression speeds worked well for the Barrett Hand only. Visualization of the feature spaces using PCA showed that the gripper morphology and the action parameters were the main source of variance, rendering generalization across embodiment or grasp configurations very hard. On the highly challenging dataset consisting of polyurethane foams alone, only the Barrett Hand achieved excellent performance. Tactile sensors can thus provide a key advantage even if recognition is based on stiffness rather than shape. The dataset with 24000 measurements is publicly available.

翻译：在触觉物体识别中，夹爪具身特性、动作参数与传感通道的影响尚未得到系统研究。我们使用两只仿人手和两只两指夹爪抓取两组可变形物体。在物体分类任务中发现：（i）在所有分类器中，基于感官特征的SVM和基于原始时间序列的LSTM在所有夹爪上表现最佳；（ii）较快的压缩速度会降低性能；（iii）对不同抓取构型的泛化能力有限；仅Barrett Hand能良好迁移至不同压缩速度。通过PCA可视化特征空间表明，夹爪形态与动作参数是主要方差来源，导致跨具身或抓取构型的泛化极为困难。在仅含聚氨酯泡沫的高难度数据集中，仅Barrett Hand实现了优异性能。因此，即使识别基于刚度而非形状，触觉传感器仍可提供关键优势。包含24000次测量的数据集已公开。