Single-grasp deformable object discrimination: the effect of gripper morphology, sensing modalities, and action parameters

TL;DR

This study systematically investigates how gripper design, sensing modalities, and action parameters affect deformable object discrimination, revealing the importance of tactile sensing and the challenges of generalization across different embodiments.

Contribution

It provides a comprehensive analysis of the influence of gripper morphology and action parameters on deformable object classification, including a large publicly available dataset.

Findings

SVM on sensory features and LSTM on raw data perform best across grippers.

Faster compression speeds reduce classification accuracy.

Generalization across different grasp configurations is limited.

Abstract

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.