単一把握による変形物体の識別：グリッパ形態・センシング手法・動作パラメータの影響
Single-grasp deformable object discrimination: the effect of gripper morphology, sensing modalities, and action parameters
2種類のロボットハンドと2種類の2本指グリッパで変形物体を把持し、触覚による物体分類性能がグリッパ形状や圧縮速度などの動作条件によってどう変わるかを体系的に調べた研究。
著者: Michal Pliska, Shubhan Patni, Michal Mares, Pavel Stoudek, Zdenek Straka, Karla Stepanova, Matej Hoffmann
分類: cs.RO
原文アブストラクト
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 gripper morphology and action parameters were the main source of variance, making generalization across embodiment or grip configurations very difficult. 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 data set with 24,000 measurements is publicly available.
