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We studied the discrimination of deformable objects by grasping them using 4different robot hands / grippers: Barrett hand (3 fingers with adjustableconfiguration, 96 tactile, 8 position, 3 torque sensors), qb SoftHand (5fingers, 1 motor, position and current feedback), and two industrial typeparallel jaw grippers with position and effort feedback (Robotiq 2F-85 andOnRobot RG6). A set of 9 ordinary objects differing in size and stiffness andanother highly challenging set of 20 polyurethane foams differing in materialproperties only was used. We systematically compare the grippers' performance,together with the effects of: (1) type of classifier (k-NN, SVM, LSTM)operating on raw time series or on features, (2) action parameters (graspingconfiguration and speed of squeezing), (3) contribution of sensory modalities.Classification results are complemented by visualization of the data using PCA.We found: (i) all the grippers but the qb SoftHand could reliably distinguishthe ordinary objects set; (ii) Barrett Hand reached around 95% accuracy on thefoams; OnRobot RG6 around 75% and Robotiq 2F-85 around 70%; (iii) across allgrippers, SVM over features and LSTM on raw time series performed best; (iv)faster compression speeds degrade classification performance; (v) transferlearning between compression speeds worked well for the Barrett Hand only;transfer between grasping configurations is limited; (vi) ablation experimentsprovided intriguing insights -- sometimes a single sensory channel suffices fordiscrimination. Overall, the Barrett Hand as a complex and expensive devicewith rich sensory feedback provided best results, but uncalibrated parallel jawgrippers without tactile sensors can have sufficient performance forsingle-grasp object discrimination based on position and effort data only.Transfer learning between the different robot hands remains a challenge.

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