Re-evaluating Parallel Finger-tip Tactile Sensing for Inferring Object Adjectives: An Empirical Study

Abstract-- Finger-tip tactile sensors are increasingly used for robotic sensing to establish stable grasps and to infer object properties. Promising performance has been shown in a number of works for inferring adjectives that describe the object, but there remains a question about how each taxel contributes to the performance. This paper explores this question with empirical experiments, leading insights for future finger-tip tactile sensor usage and design. In recent years, with the developments of tactile sensors and machine learning techniques, finger-tip tactile sensors are increasingly used for robotic tasks such as adjective classification [1]. Promising performance has been shown for adjective classification [1], [2], [3] or learning adjective distributions [4].